Variation in English free essay sample

English like any other language, like every language, is subject to variation. This variation can be complex and at times subtle. This text provides us with information about the principal ways in which British and Irish English speech varies and, just as importantly, the non-linguistic (social, geographical) factors which condition variation. Variation in pronunciation RP Dialect: refers to the varieties distinguished from each other by differences of grammar and vocabulary. Accent: on the other hand, refers to variations in pronunciation. RP stands for â€Å"received pronunciation†. Received here is understood in its nineteeth-century sense of accepted in the most polite circles of society. RP remained the accent of those in the upper reaches of the social scale, as measured by education, income and profession, or title. It has traditionally been the accent of those educated at public schools. It is not the accent of any particular region. It enjoys the highest prestige in England. We will write a custom essay sample on Variation in English or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page It is estimated that only about 3 to 5 percent of the population of England speak RP. Since it is clearly a minority accent, why then, is it the only British accent taught to foreign learners? Social prestige; RP is often associated with high social status, wealth and power of its speakers. It is often considered the best, the clearest, and even the most beautiful accent. Because of its use on radio and television within Britain RP has become probably the most widely understood of all accents. Moreover, it is by far the most thoroughly described of British accents. Language change Accents change with time. In RP, for example, there has for some considerable time been a tendency, through a process known by linguists as smoothing, for certain triphthongs and diphthongs to become monophthongs. There is not a perfect correlation between age and pronunciation. Some RP speakers will regard the distinguishing features of the advanced variety of the accent as â€Å"affected† and will not alter their own speech, at least not until these features become more general with the passage of time. Other RP speakers will be only too ready to integrate them into their own speech. As there are early or conservative adopters, it would be misleading to say there is only one, fixed form of the accent, since at any stage the accent will be a mixture of traditional and innovative features. The long-standing association of RP with affection, social snobbery, aloofness, and so on, is increasingly out of keeping with the kind of image many of the accent’s younger speakers would wish to project of themselves. This trend can be noticed in the influence of non-standard and foreign accents and dialects, along with a general deterioration in other modes of behaviour (dress, manner, respect for elders) and a perceived rise of sloppiness in pronunciation and disregard for proper grammar. The decline of RP and standard English can be reflected in the rise of a new variety first dubbed â€Å"Estuary English† by Rosewarne (1984) The usual definition of Estuary English is an amalgam of RP and working-class London speech (Cockney). This seemed to be a neutral variety which provided the opportunity for lower-class speakers to appear higher status than they are, and for middle-and upper-class speakers to appear lower status than they are. A particular salient, widely discussed and often heavily stigmatized Estuary form is glottal stop / / as a pronunciation of / t /in certain contexts. Another example of a feature entering modern RP which probably has its origins in a non-standard accent is the (w)-like labio dental approximant (v)as a pronunciation of / / that appears to be spreading fairly fast in British English. Another example of a change is at a relatively early stage in its progress is the so-called High-rising, the high rise terminal (HRT), Australian question intonation (AQI)or uptalk. This phenomenon is often defined as the use, in statements, of the rising intonation pattern normally associated with questions. Some people interpret high-rising tone as an indication of general uncertainty and lack of confidence on the part of the young people who use it. These people have also attributed this influence to Australian and New Zealand English, both of which share this property. This suggestion has been raised because it has become so common for young British school leavers to travel independently overseas on the backpacking trail, often for extended periods. The use of high-rising may have come to act as a badge of well-travelled or wordly status. In tandem with its association with the appealing stereotype of Australians, and New Zealanders as relaxed, friendly, open, sporty, fun-loving, etc. The claim that the use of HRT patterns indicates uncertainty is also probably untenable, given that such patterns may be used when giving information about which the speaker cannot have any doubt (name, personal information, etc. ) Stylistic variation It is the variation conditioned by speakers? perceptions of the situation in which they are speaking. There is variation among RP speakers and in the pronunciation of individual speakers.

Overview of Partial Classes in Visual Basic .NET

Overview of Partial Classes in Visual Basic .NET Partial Classes are a feature of VB.NET that is used almost everywhere, but theres not much written about it. This might be because there are not a lot of obvious developer applications for it yet. The primary use is in the way ASP.NET and VB.NET solutions are created in Visual Studio where its one of those features that is normally hidden. A partial class is simply a class definition that is split into more than one physical file. Partial classes dont make a difference to the compiler because all the files that make up a class are simply merged into a single entity for the compiler. Since the classes are just merged together and compiled, you cant mix languages. That is, you cant have one partial class in C# and another in VB. You cant span assemblies with partial classes either. They all have to be in the same assembly. This is used a lot by Visual Studio itself, especially in web pages where it is a key concept in code behind files. Well see how this works in a Visual Studio, but understanding what changed in Visual Studio 2005 when it was introduced is a good starting point. In Visual Studio 2003, the hidden code for a Windows application was all in a section called a Region marked Windows Form Designer generated code. But it was still all there in the same file and it was easy to view, and change, the code in the Region. All of the code is available to your application in .NET. But since some of it is code that you should almost never mess with, it was kept in that hidden Region. (Regions can still be used for your own code, but Visual Studio doesnt use them anymore.) In Visual Studio 2005 (Framework 2.0), Microsoft did approximately the same thing, but they hid the code in a different place: a partial class in a separate file. You can see this at the bottom of the illustration below: Click Here to display the illustrationClick the Back button on your browser to return One of the syntax differences between Visual Basic and C# right now is that C# requires that all partial classes be qualified with the keyword Partial but VB does not. Your main form in VB.NET doesnt have any special qualifiers. But the default class statement for an empty Windows application looks like this using C#: public partial class Form1 : Form Microsofts design choices on things like this are interesting. When Paul Vick, Microsofts VB designer, wrote about this design choice in his blog Panopticon Central, the debate about it in the comments went on for pages and pages. Lets see how all this works with real code on the next page. On the previous page, the concept of partial classes was explained. We convert a single class into two partial classes on this page. Heres an example class with one method and one property in a VB.NET project Public Class CombinedClass   Ã‚  Ã‚  Private m_Property1 As String   Ã‚  Ã‚  Public Sub New(ByVal Value As String)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  m_Property1 Value   Ã‚  Ã‚  End Sub   Ã‚  Ã‚  Public Sub Method1()   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  MessageBox.Show(m_Property1)   Ã‚  Ã‚  End Sub   Ã‚  Ã‚  Property Property1() As String   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Get   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Return m_Property1   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  End Get   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Set(ByVal value As String)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  m_Property1 value   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  End Set   Ã‚  Ã‚  End Property End Class This class can be called (for example, in the Click event code for a Button object) with the code: Dim ClassInstance As New _   Ã‚  Ã‚  CombinedClass(About Visual Basic Partial Classes) ClassInstance.Method1() We can separate the properties and methods of the class into different physical files by adding two new class files to the project. Name the first physical file Partial.methods.vb and name the second one Partial.properties.vb. The physical file names have to be different but the partial class names will be the same so Visual Basic can merge them when the code is compiled. Its not a syntax requirement, but most programmers are following the example in Visual Studio of using dotted names for these classes. For example, Visual Studio uses the default name Form1.Designer.vb for the partial class for a Windows form. Remember to add the Partial keyword for each class and change the internal class name (not the file name) to the same name. I used the internal class name: PartialClass. The illustration below shows all of the code for the example and the code in action. Click Here to display the illustrationClick the Back button on your browser to return Visual Studio hides partial classes such as Form1.Designer.vb. On the next page, we learn how to do that with the partial classes we just created. The previous pages explain the concept of partial classes and show how to code them. But Microsoft uses one more trick with the partial classes generated by Visual Studio. One of the reasons for using them is to separate application logic from UI (user interface) code. In a large project, these two types of code might even be created by different teams. If theyre in different files, they can be created and updated with a lot more flexibility. But Microsoft goes one more step and hides the partial code in Solution Explorer as well. Suppose we wanted to hide the methods and properties partial classes in this project? Theres a way, but its not obvious and Microsoft doesnt tell you how. One of the reasons you dont see the use of partial classes recommended by Microsoft is that its not really supported very well in Visual Studio yet. To hide the Partial.methods.vb and Partial.properties.vb classes that we just created, for example, requires a change in the vbproj file. This is an XML file that isnt even displayed in Solution Explorer. You can find it with Windows Explorer along with your other files. A vbproj file is shown in the illustration below. Click Here to display the illustrationClick the Back button on your browser to return The way were going to do this is to add a root class that is completely empty (only the Class header and End Class statement are left) and make both of our partial classes dependent on it. So add another class named PartialClassRoot.vb and again change the internal name to PartialClass to match the first two. This time, I have not used the Partial keyword just to match the way Visual Studio does it. Heres where a little knowledge of XML will come in very handy. Since this file will have to be updated manually, you have to get the XML syntax right. You can edit the file in any ASCII text editor - Notepad works just fine - or in an XML editor. It turns out that you have a great one in Visual Studio and thats what is shown in the illustration below. But you cant edit the vbproj file at the same time that youre editing the project its in. So close the project and open only the vbproj file. You should see the file displayed in the edit window as shown in the illustration below. (Note the Compile elements for each class. DependentUpon sub-elements must be added exactly as shown in the illustration below. This illustration was created in VB 2005 but it has been tested in VB 2008 as well.) Click Here to display the illustrationClick the Back button on your browser to return For many of us, its probably enough to know that partial classes are there, just so we know what they are when were trying to track down a bug in the future. For large and complex systems development, they could be a small miracle because they can help organize code in ways that would have been impossible before. (You can also have partial structures and partial interfaces!) But some people have concluded that Microsoft invented them just for internal reasons - to make their code generation work better. Author Paul Kimmel even went so far as to suggest that Microsoft actually created partial classes to lower their costs by making it easier to outsource development work around the world. Maybe. Its the kind of thing they might do.

Hoitsus Mount Fugi by Sakai Hoitsu Essay Example | Topics and Well Written Essays - 1500 words

Hoitsus Mount Fugi by Sakai Hoitsu - Essay Example As a people of tradition and ritual, the Japanese put meaning into their artistic works that was defined by symbols that were readily available to all of those within the culture. Through the nature of this strong sense of symbolism, the people of Japan have left a long body of work that establishes patterns and belief systems about their culture. The artwork of Hoitsu and the writing of Basho are both a part of this heritage and contribute to the discourse through a connected cultural dialogue. The idea of meisho as it is described by Machotka as it refers to painting is that it represented a famous place, connecting the viewer to the image through heritage and culture. The fascinating element of meisho and its application in painting is that artistic works not only would often use the idea in their works, but in pre-modern Japan there was not the idea of creating a landscape that was not also representative of meisho. Machotka states that â€Å"Japanese painting tradition did not appreciate places from outside of the vocabulary of meisho as an independent art theme as well as it did not demonstrate general concern in realistic description of views, which would evoke their authenticity†.1 Machotka also discusses the idea that many of the visual references were close to unidentifiable, but because they were associated with literary references, poetic descriptions of places from well known works, that identified them to the basic associations that were visually availa ble.2 Looking at the two paneled screen from the perspective of the viewer, the simplicity of the depiction is the first thing to strike the eye. There is a feeling of movement, but it is not harried nor is it in tension.

Ecommerce federal express Essay Example | Topics and Well Written Essays - 500 words

Ecommerce federal express - Essay Example Choosing a service, plus an option is the first step to placing an order with FedEx after a customer has opened an account, where services to choose from include U.S. packages and envelopes, international packages and envelopes, freight services, C.O.D. and other options. The second step, packaging the shipment, as stated in its website provides customers with packaging options, help tips for preparing the package for shipment, and packaging services. Processing the shipment, the third step, enables customers to use a FedEx electronic shipping tool such as the FedEx shipping manager which â€Å"helps a customer quickly and easily complete all of his/her shipping documents — from airbills and air waybills to bar-code labels, and more — online; apart from the FedEx shipping manager, there are â€Å"other FedEx ® electronic shipping tools simplify your shipping process and paperwork by: providing online airbills and air waybills, Commercial Invoices, and other customs documentation; preventing delays  caused by  inaccurate or incomplete documentation; saving you time  so you can  focus on more important matters† as stated in its website. E-commerce provides competitive advantage in both the outside-in and inside-out perspective of building an organizational capability. From an outside-in perspective, e-commerce allows FedEx to build relationship with its FedEx brand and its customers through its website which is part of its direct marketing efforts, allowing customers to interact with the company as it provides faster access for them to avail of the services, while it also works to reinforce the brand by allowing customers to check if the delivery of the availed service has been met according to what FedEx has promised them. The database also allows FedEx to keep track of its customers, know their preferences and

Sexed Crime Essay Example | Topics and Well Written Essays - 1750 words

Sexed Crime - Essay Example Significantly, changes in the meaning of 'sex' as well as challenges to the speaking positions of the dominant groups have led to an ultimate shift from the use of sex crime to sexed crime. One of the most essential concerns of Adrian Howe's Sexed crime in the News has been to analyse whether the concept of sexed crime has a potential to expand one's understanding of sexual violence and Howe claims that "calling sex crime 'sexed crime' does have interesting, destabilising effects." (Howe 1998, P. 6). According to Howe, the complacency and self-evidence of sex crime is disturbed by the use of sexed crime instead of sex crime. In an understanding of the other important destabilising effects, it becomes lucid that speaking about sexed rather than sex crime "problematises the 'sex' of crimes of violence", "acknowledges that women are not the only sex - men have a sex too," and "speaking about sexed crime sexes violence in the sense of asking questions about the fundamental, but often ign ored, sexed and sexual aspects of that violence." (Howe 1998, P. 6). ... Various pertinent questions crop up in an attempt to analyse the difference between the concepts 'sex crime' and 'sexed crime' such as what is 'sexed' crime, can sexual assaults be considered as sex crime, is family violence sex crime, etc. According to Howe, there are several destabilising effects of 'calling sex crime sexed crime' and the latter is broader in scope than the former. Thus, calling sex crime sexed crime can disturb the self-evidence of sex crime, and sexed crime covers all forms of violence in which the gender of the victim, as well as the perpetrator, is significant to the violent act. "Sexed crimeis violence which can only be fully understood within the context of relationships which are profoundly sexed but which are not often recognised as such, precisely because they are dismissed as having something to do with a vaguely defined, amorphous 'gender'. The point of questioning the sex of sex crime is to challenge the taken-for-granted ways in which sexed and gendere d relations are represented in public discourses such as those of the media." (Howe 1998, P. 6). Therefore, it is essential to realise that Howe (1998), in her Sexed crime in the News, comes up with a new and somewhat different approach to the issue of sex crime, which is critical of the idea purported by Keith Soothill and Sylvia Walby in Sex Crime in the News (1991). Sex crime is a term which is commonly used in order to refer to the sexual assaults against females across the globe and there have been reference to the age of sex crime in the modern times. Sex and violence in the contemporary world have been closely interconnected in the recent day world and they are

The Concept of Probability in Mathematics

The Concept of Probability in Mathematics Probability is a way of expressing knowledge or belief that an event will occur or has occurred. The concept has been given an exact mathematical meaning in probability theory, which is used extensively in such areas of study as mathematics, statistics, finance, gambling, science, and philosophy to draw conclusions about the likelihood of potential events and the underlying mechanics of complex systems. The word probability does not have a consistent direct definition. In fact, there are two broad categories of probability interpretations, whose adherents possess different views about the fundamental nature of probability. The word Probability derives from Latin word probabilitas that can also mean probity, a measure of the authority of a witness in a legal case in Europe, and often correlated with the witnesss nobility. In a sense, this differs much from the modern meaning of probability, which, in contrast, is used as a measure of the weight of empirical evidence, and is arrived at from inductive reasoning and statistical inference. History: The scientific study of probability is a modern development. Gambling shows that there has been an interest in quantifying the ideas of probability for millennia, but exact mathematical descriptions of use in those problems only arose much later. According to Richard Jeffrey, Before the middle of the seventeenth century, the term probable meant approvable, and was applied in that sense, univocally, to opinion and to action. A probable action or opinion was one such as sensible people would undertake or hold, in the circumstances.[4] However, in legal contexts especially, probable could also apply to propositions for which there was good evidence. Aside from some elementary considerations made by Girolamo Cardano in the 16th century, the doctrine of probabilities dates to the correspondence of Pierre de Fermat and Blaise Pascal (1654). Christiaan Huygens (1657) gave the earliest known scientific treatment of the subject. Jakob Bernoullis and Abraham de Moivres Doctrine of Chances (1718) treated the subject as a branch of mathematics. See Ian Hackings The Emergence of Probability and James Franklins The Science of Conjecture for histories of the early development of the very concept of mathematical probability. The theory of errors may be traced back to Roger Cotes but a memoir prepared by Thomas Simpson in 1755 (printed 1756) first applied the theory to the discussion of errors of observation. The reprint (1757) of this memoir lays down the axioms that positive and negative errors are equally probable, and that there are certain assignable limits within which all errors may be supposed to fall; continuous errors are discussed and a probability curve is given. Pierre-Simon Laplace (1774) made the first attempt to deduce a rule for the combination of observations from the principles of the theory of probabilities. He represented the law of probability of errors by a curve y = à Ã¢â‚¬  (x), x being any error and y its probability.He also gave (1781) a formula for the law of facility of error (a term due to Lagrange, 1774), but one which led to unmanageable equations. Daniel Bernoulli (1778) introduced the principle of the maximum product of the probabilities of a system of concurrent errors. The method of least squares is due to Adrien-Marie Legendre (1805), who introduced it in his New Methods for Determining the Orbits of Comets. In ignorance of Legendres contribution, an Irish-American writer, Robert Adrain, editor of The Analyst (1808), first deduced the law of facility of error, h being a constant depending on precision of observation, and c a scale factor ensuring that the area under the curve equals 1. He gave two proofs, the second being essentially the same as John Herschels (1850). Gauss gave the first proof which seems to have been known in Europe (the third after Adrains) in 1809. Further proofs were given by Laplace (1810, 1812), Gauss (1823), James Ivory (1825, 1826), Hagen (1837), Friedrich Bessel (1838), W. F. Donkin (1844, 1856), and Morgan Crofton (1870). Other contributors were Ellis (1844), De Morgan (1864), Glaisher (1872), and Giovanni Schiaparelli (1875). Peterss (1856) formula for r, the probable error of a single observation, is well known. In the nineteenth century authors on the general theory included Laplace, Sylvestre Lacroix (1816), Littrow (1833), Adolphe Quetelet (1853), Richard Dedekind (1860), Helmert (1872), Hermann Laurent (1873), Liagre, Didion, and Karl Pearson. Augustus De Morgan and George Boole improved the exposition of the theory. Andrey Markov introduced the notion of Markov chains (1906) playing an important role in theory of stochastic processes and its applications. The modern theory of probability based on the measure theory was developed by Andrey Kolmogorov (1931). On the geometric side, contributors to The Educational Times were influential. Types of probability: There are basically four types of probabilities, each with its limitations. None of these approaches to probability is wrong, but some are more useful or more general than others. Classical Probability: The classical interpretation owes its name to its early and august pedigree. Championed by Laplace, and found even in the works of Pascal, Bernoulli, Huygens, and Leibniz, it assigns probabilities in the absence of any evidence, or in the presence of symmetrically balanced evidence. The classical theory of probability applies to equally probable events, such as the outcomes of tossing a coin or throwing dice; such events were known as equipossible. probability = number of favourable equipossibilies / total number of relevant equipossibilities. Logical probability: Logical theories of probability retain the classical interpretations idea that probabilities can be determined a priori by an examination of the space of possibilities. Subjective probability: A probability derived from an individuals personal judgment about whether a specific outcome is likely to occur. Subjective probabilities contain no formal calculations and only reflect the subjects opinions and past experience. Subjective probabilities differ from person to person. Because the probability is subjective, it contains a high degree of personal bias. An example of subjective probability could be asking New York Yankees fans, before the baseball season starts, the chances of New York winning the world series. While there is no absolute mathematical proof behind the answer to the example, fans might still reply in actual percentage terms, such as the Yankees having a 25% chance of winning the world series. In everyday speech, we express our beliefs about likelihoods of events using the same terminology as in probability theory. Often, this has nothing to do with any formal definition of probability, rather it is an intuitive idea guided by our experience, and in some cases statistics. Some Of the Examples Of Probability: X says Dont buy the avocados here; about half the time, theyre rotten. X is expressing his belief about the probability of an event that an avocado will be rotten based on his personal experience. Y says I am 95% certain the capital of Spain is Barcelona. Here, the belief Y is expressing is only a probability from his point of view, because only he does not know that the capital of Spain is Madrid (from our point of view, the probability is 100%). However, we can still view this as a subjective probability because it expresses a measure of uncertainty. It is as though Y is saying in 95% of cases where I feel as sure as I do about this, I turn out to be right. Z says There is a lower chance of being shot in Omaha than in Detroit. Z is expressing a belief based (presumably) on statistics. Dr. A says to Christina, There is a 75% chance that you will live. Dr. A is basing this off of his research. Probability can also be expressed in vague terms. For example, someone might say it will probably rain tomorrow. This is subjective, but implies that the speaker believes the probability is greater than 50%. Subjective probabilities have been extensively studied, especially with regards to gambling and securities markets. While this type of probability is important, it is not the subject of this book. There are two standard approaches to conceptually interpreting probabilities. The first is known as the long run (or the relative frequency approach) and the subjective belief (or confidence approach). In the Frequency Theory of Probability, probability is the limit of the relative frequency with which an event occurs in repeated trials (note that trials must be independent). Frequentists talk about probabilities only when dealing with experiments that are random and well-defined. The probability of a random event denotes the relative frequency of occurrence of an experiments outcome, when repeating the experiment. Frequentists consider probability to be the relative frequency in the long run of outcomes. Physical probabilities, which are also called objective or frequency probabilities, are associated with random physical systems such as roulette wheels, rolling dice and radioactive atoms. In such systems, a given type of event (such as the dice yielding a six) tends to occur at a persistent rate, or relative frequency, in a long run of trials. Physical probabilities either explain, or are invoked to explain, these stable frequencies. Thus talk about physical probability makes sense only when dealing with well defined random experiments. The two main kinds of theory of physical probability are frequentist accounts and propensity accounts. Relative frequencies are always between 0% (the event essentially never happens) and 100% (the event essentially always happens), so in this theory as well, probabilities are between 0% and 100%. According to the Frequency Theory of Probability, what it means to say that the probability that A occurs is p% is that if you repeat the experiment over and over again, independently and under essentially identical conditions, the percentage of the time that A occurs will converge to p. For example, under the Frequency Theory, to say that the chance that a coin lands heads is 50% means that if you toss the coin over and over again, independently, the ratio of the number of times the coin lands heads to the total number of tosses approaches a limiting value of 50% as the number of tosses grows. Because the ratio of heads to tosses is always between 0% and 100%, when the probability exists it must be between 0% and 100%. In the Subjective Theory of Probability, probability measures the speakers degree of belief that the event will occur, on a scale of 0% (complete disbelief that the event will happen) to 100% (certainty that the event will happen). According to the Subjective Theory, what it means for me to say that the probability that A occurs is 2/3 is that I believe that A will happen twice as strongly as I believe that A will not happen. The Subjective Theory is particularly useful in assigning meaning to the probability of events that in principle can occur only once. For example, how might one assign meaning to a statement like there is a 25% chance of an earthquake on the San Andreas fault with magnitude 8 or larger before 2050? It is very hard to use either the Theory of Equally Likely Outcomes or the Frequency Theory to make sense of the assertion. Bayesians, however, assign probabilities to any statement whatsoever, even when no random process is involved. Probability, for a Bayesian, is a way to represent an individuals degree of belief in a statement, given the evidence. Evidential probability, also called Bayesian probability, can be assigned to any statement whatsoever, even when no random process is involved, as a way to represent its subjective plausibility, or the degree to which the statement is supported by the available evidence. On most accounts, evidential probabilities are considered to be degrees of belief, defined in terms of dispositions to gamble at certain odds. The four main evidential interpretations are the classical interpretation, the subjective interpretation, the epistemic or inductive interpretation, and the logical interpretation. Theory: Like other theories, the theory of probability is a representation of probabilistic concepts in formal terms-that is, in terms that can be considered separately from their meaning. These formal terms are manipulated by the rules of mathematics and logic, and any results are then interpreted or translated back into the problem domain. There have been at least two successful attempts to formalize probability, namely the Kolmogorov formulation and the Cox formulation. In Kolmogorovs formulation, sets are interpreted as events and probability itself as a measure on a class of sets. In Coxs theorem, probability is taken as a primitive and the emphasis is on constructing a consistent assignment of probability values to propositions. In both cases, the laws of probability are the same, except for technical details. There are other methods for quantifying uncertainty, such as the Dempster-Shafer theory or possibility theory, but those are essentially different and not compatible with the laws of probability as they are usually understood. Mathematical Treatment: In mathematics, a probability of an event A is represented by a real number in the range from 0 to 1 and written as P(A), p(A) or Pr(A). An impossible event has a probability of 0, and a certain event has a probability of 1. However, the converses are not always true: probability 0 events are not always impossible, nor probability 1 events certain. The opposite or complement of an event A is the event (that is, the event of A not occurring); its probability is given by P(not A) = 1 P(A). As an example, the chance of not rolling a six on a six-sided die is 1 (chance of rolling a six) . If both the events A and B occur on a single performance of an experiment this is called the intersection or joint probability of A and B, denoted as . If two events, A and B are independent then the joint probability is For example: if two coins are flipped the chance of both being heads is If either event A or event B or both events occur on a single performance of an experiment this is called the union of the events A and B denoted as . If two events are mutually exclusive then the probability of either occurring is For example, the chance of rolling a 1 or 2 on a six-sided die is If the events are not mutually exclusive then Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P(A|B), and is read the probability of A, given B. It is defined by If P(B) = 0 then is undefined. Applications: Two major applications of probability theory in everyday life are in risk assessment and in trade on commodity markets. Governments typically apply probabilistic methods in environmental regulation where it is called pathway analysis, often measuring well-being using methods that are stochastic in nature, and choosing projects to undertake based on statistical analyses of their probable effect on the population as a whole. A good example is the effect of the perceived probability of any widespread Middle East conflict on oil prices which have ripple effects in the economy as a whole. An assessment by a commodity trader that a war is more likely vs. less likely sends prices up or down, and signals other traders of that opinion. Accordingly, the probabilities are not assessed independently nor necessarily very rationally. The theory of behavioural finance emerged to describe the effect of such groupthink on pricing, on policy, and on peace and conflict. It can reasonably be said that the discovery of rigorous methods to assess and combine probability assessments has had a profound effect on modern society. Accordingly, it may be of some importance to most citizens to understand how odds and probability assessments are made, and how they contribute to reputations and to decisions, especially in a democracy. Another significant application of probability theory in everyday life is reliability. Many consumer products, such as automobiles and consumer electronics, utilize reliability theory in the design of the product in order to reduce the probability of failure. The probability of failure may be closely associated with the products warranty. Probability Of Winning A Lottery: Everyone knows that the probability of winning the lottery is a pretty big long shot. How long, however, you probably never really thought about. Your actual odds of winning the lottery depend on where you play, but single state lotteries usually have odds of about 18 million to 1 while multiple state lotteries have odds as high as 120 million to 1. If you have ever thought youd win the lottery, youre not alone. About one out of every three people in the United States think that winning the lottery is the only way to become financially secure in their life. This is a frightening statistic when you sit down and consider what the above odds really mean. Its time to take a long hard look at the chances of you winning the lottery. While winning the lottery may be something that you want, to show you your chances well take a look at a number of remote occurrences that you probably wouldnt like to have happen to you and probably dont think will ever happen to you but are still much more likely to happen to you than winning the lottery. How about the classic odds of being struck by lightning? The actual probability of this happening varies from year to year, but as a good estimate, the National Safety Council says between 70 and 120 people a year die in the US by lightning so lets take 100 as our base. With the US population being approximately 265 million people, that means that the chances of being killed by lightning are roughly 2,650,000 to 1. Not very likely. However you are still 6 to 45 times more likely to die from a lightning strike than you would be to win the lottery. Now nobody really wants to die from flesh eating bacteria, and with odds at about 1 million to 1, the chances that you will die that way are pretty slim. Then again, you are 18 to 120 times more likely to die this way than to win the lottery. What are the chances that if youre playing with a group of four that two of you will get a hole-in-one on the exact same hole? At 17 million to 1, theyre better than the chances of you winning the lottery. What about dying from a snake bite or bee sting? It probably isnt a way that you have imagined that you would leave the earth. Youre a whopping 180 to 1,200 times more likely to die from one of these incidents than win the lottery. Thats because the probability of dying from a snake bite or bee sting is about 100,000 to 1. Now I know that you are not a bad person and you dont imagine finding yourself on death row for a crime you committed anytime soon. Still, its a lot more likely that you will be legally executed than win the lottery. In fact, you are 30,000% to 200,000% more likely to die in a legal execution than to win the lottery. If none of the above has convinced you to stop playing the lottery, then Ill bring out my favorite lottery fact. If you drive 10 miles to purchase your lottery ticket, its three to twenty times more likely for you to be killed in a car accident along the way than to win the jackpot. Flipping Of Coin: Coin flipping or coin tossing is the practice of throwing a coin in the air to choose between two alternatives, sometimes to resolve a dispute between two parties. It is a form of sortition which inherently has only two possible and equally likely outcomes. Experimental and theoretical analysis of coin tossing has shown that the outcome is predictable. During coin flipping the coin is tossed into the air such that it rotates end-over-end several times. Either beforehand or when the coin is in the air, an interested party calls heads or tails, indicating which side of the coin that party is choosing. The other party is assigned the opposite side. Depending on custom, the coin may be caught, caught and inverted, or allowed to land on the ground. When the coin comes to rest, the toss is complete and the party who called or was assigned the face-up side is declared the winner. If the outcome is unclear the toss is repeated; for example the coin may, very rarely, land on edge, or fall down a drain. The coin may be any type as long as it has two distinct sides; it need not be a coin as such. Human intuition about conditional probability is often very poor and can give rise to some seemingly surprising observations. For example, if the successive tosses of a coin are recorded as a string of H and T, then for any trial of tosses, it is twice as likely that the triplet TTH will occur before THT than after it. It is three times as likely that THH will precede HHT. Are we likely to be struck by lightning? In the United States, an average of 80 people are killed by lightning each year. Considering being killed by lightning to be our favorable outcome (not such a favorable outcome!), the sample space contains the entire population of the United States (about 250 million). If we assume that all the people in our sample space are equally likely to be killed by lightning (so people who never go outside have the same chance of being killed by lightning as those who stand by flagpoles in large open fields during thunderstorms), the chance of being killed by lightning in the United States is equal to 80/250 million, or a probability of about .000032%. Clearly, you are much more likely to die in a car accident than by being struck by lightning. Probability in Our Lives: A basic understanding of probability makes it possible to understand everything from batting averages to the weather report or your chances of being struck by lightning! Probability is an important topic in mathematics because the probability of certain events happening or not happening can be important to us in the real world. Weather forecasting: Suppose a person wants to go on a picnic this afternoon, and the weather report says that the chance of rain is 70%? Will he ever wonder where that 70% came from? Forecasts like these can be calculated by the people who work for the National Weather Service when they look at all other days in their historical database that have the same weather characteristics (temperature, pressure, humidity, etc.) and determine that on 70% of similar days in the past, it rained. As weve seen, to find basic probability we divide the number of favorable outcomes by the total number of possible outcomes in our sample space. If were looking for the chance it will rain, this will be the number of days in our database that it rained divided by the total number of similar days in our database. If our meteorologist has data for 100 days with similar weather conditions (the sample space and therefore the denominator of our fraction), and on 70 of these days it rained (a favorable outcome), the probability of rain on the next similar day is 70/100 or 70%. Since a 50% probability means that an event is as likely to occur as not, 70%, which is greater than 50%, means that it is more likely to rain than not. But what is the probability that it wont rain? Remember that because the favourable outcomes represent all the possible ways that an event can occur, the sum of the various probabilities must equal 1 or 100%, so 100% 70% = 30%, and the probability that it wont rain is 30%. Bernoulli Trials On Probability: It happens very often in real life that an event may have only two outcomes that matter. For example, either you pass an exam or you do not pass an exam, either you get the job you applied for or you do not get the job, either your flight is delayed or it departs on time, etc. The probability theory abstraction of all such situations is a Bernoulli trial. Bernoulli trial is an experiment with only two possible outcomes that have positive probabilities p and q such that p + q = 1. The outcomes are said to be success and failure, and are commonly denoted as S and F or, say, 1 and 0. For example, when rolling a die, we may be only interested whether 1 shows up, in which case,naturally, P(S) = 1/6 and P(F) = 5/6. If, when rolling two dice, we are only interested whether the sum on two dice is 11, P(S) = 1/18, P(F) = 17/18. The Bernoulli process is a succession of independent Bernoulli trials with the same probability of success. Uses Of Probability In Our Daily Lives: I think we use probability routinely in our daily lives. When you get into a car and drive on public roads, we often assume that we have a low probability of being hit by another car. When you pull out onto a busy street crossing 2 lanes of traffic, you judge the speed of the traffic in those lanes. You assume you have a high probability of judging that speed correctly when you cross those lanes. If you did not make that assumption, you probably would not attempt to cross the lanes for fear of being hit by another car. We assume that we have a low probability of being hit by lightning or a meteor. When you eat with your hands, you assume your probability of getting sick from germs on your hands is low. Or you wouldnt eat with your hands. You could say the same of eating in a restaurant with reference to food you didnt prepare yourself. Within assuming many probabilities, I think wed constantly live in fear of what horrible things might happen to us. Summary of probabilities: Event Probability A not A A or B A and B A given B Other Cases Where Probability Can Be Observed: Youve seen it happen many times-a player in a dice game claims she is due for doubles; strangers discover that they have a mutual acquaintance and think that this must be more than a chance meeting; a friend plays the lottery obsessively or enters online contests with a persistent dream of winning. All these behaviors reflect how people perceive probability in daily life. People who lack an accurate sense of probability are easily drawn in by false claims and pseudoscience, are vulnerable to get-rich-quick schemes, and exhibit many of the behaviors mentioned above. The modeling and measurement of probabilities are fundamentals of mathematics that can be applied to the world around us. Every event, every measurement, every game, every accident, and even the nature of matter itself is understood through probabilistic models, yet few people have a good grasp of the nature of probability. Frequentists talk about probabilities only when dealing with experiments that are random and well-defined. The probability of a random event denotes the relative frequency of occurrence of an experiments outcome, when repeating the experiment. Frequentists consider probability to be the relative frequency in the long run of outcomes.[1] Bayesians, however, assign probabilities to any statement whatsoever, even when no random process is involved. Probability, for a Bayesian, is a way to represent an individuals degree of belief in a statement, or an objective degree of rational belief, given the evidence. Relation to randomness: In a deterministic universe, based on Newtonian concepts, there is no probability if all conditions are known. In the case of a roulette wheel, if the force of the hand and the period of that force are known, then the number on which the ball will stop would be a certainty. Of course, this also assumes knowledge of inertia and friction of the wheel, weight, smoothness and roundness of the ball, variations in hand speed during the turning and so forth. A probabilistic description can thus be more useful than Newtonian mechanics for analyzing the pattern of outcomes of repeated rolls of roulette wheel. Physicists face the same situation in kinetic theory of gases, where the system, while deterministic in principle, is so complex (with the number of molecules typically the order of magnitude of Avogadro constant 6.02 ·1023) that only statistical description of its properties is feasible. A revolutionary discovery of 20th century physics was the random character of all physical processes that occur at sub-atomic scales and are governed by the laws of quantum mechanics. The wave function itself evolves deterministically as long as no observation is made, but, according to the prevailing Copenhagen interpretation, the randomness caused by the wave function collapsing when an observation is made, is fundamental. This means that probability theory is required to describe nature. Others never came to terms with the loss of determinism. Albert Einstein famously remarked in a letter to Max Born: I am convinced that God does not play dice. Although alternative viewpoints exist, such as that of quantum de-coherence being the cause of an apparent random collapse, at present there is a firm consensus among physicists that probability theory is necessary to describe quantum phenomena.

Red Sorghum :: essays research papers

Red Sorghum Claire Huot China’s New Cultural Scene   Ã‚  Ã‚  Ã‚  Ã‚  The film Red Sorghum was one of the most popular Fifth Generation films in China and Abroad. As an adolescent American kid, probably the average, I got to see a new perspective of China through this class. I wanted to compare the West’s interpretation with Chinas’. One of the first things I did was compare Chinese cinema to well known American cinema. Zhang Yimou’s first film as director, Red Sorghum was immensely popular at home and abroad. The film follows a popular novel with its point of view; an off-stage, present-day male narrator whose own life is ancient and minute compared to the family he was grateful to have been associated with. Compared to a classic American Movie this is very much the same. The movie I am talking about is Legends of the fall. In almost the exact same way this movie was made. An older Indian gentleman begins to tell the story he lived. He grew up in a great family, with great traditions. Then he narrates the tale of a family and its struggles through love, war, bitterness, and bad times. Its starts with that voice, on a blank screen: â€Å"I will tell you the story of my grandpa and grandma†¦Ã¢â‚¬  In LOTF an older Indian man starts telling the story of â€Å"Tristan’s stormy birth† We then see the hero at a very young age, fighting a bear. In Red Sorghum instead of the exp ected old granny, we see a beautiful, twenty-something woman who, looks very attractive.   Ã‚  Ã‚  Ã‚  Ã‚  When Nine arrives at the distillery and rallies all the men together to work, its comparable to the first time Susanna arrives at the ranch. Run by mostly men, she has some women, but the men all look at her with admiration. Right away Tristan is taken to her. When she is out riding and lassoing the cow, is like Nine rounding up the Sorghum to work. Tristan automatically chases a nearby Mustang and catches it. Much like Grandpa catching the first â€Å"thief† on the travel through the sorghum fields. He comes back with a mustang, and Grandpa now has Nine’s appreciation.   Ã‚  Ã‚  Ã‚  Ã‚  There are many script similarities, and cut similarities between these two films, yet LOTF has a more comprehensive plot, to me. Maybe its loses something in the sub-titles, that I don’t find it as intriguing as American Cinema.   Ã‚  Ã‚  Ã‚  Ã‚  One script point in particular in LOTF is the fact that the father was a former military chief, and he left that â€Å"madness† for a more peaceful life.