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A good way to understand the Binomial distribution is to consider the following problem You are given a worn and bent coin and asked to estimate the probability that the coin will turn up heads when tossed Let us call this unknown probability of heads p You toss the coin n times and record the number of times r that it turns up heads A reasonable estimate of p is rln Note that if the experiment were rerun, generating a new set of n coin tosses, we might expect the number of heads r to vary somewhat from the value measured in the first experiment, yielding a somewhat different estimate for p The Binomial distribution describes for each possible value of r (ie, from 0 to n), the probability of observing exactly r heads given a sample of n independent tosses of a coin whose true probability of heads is p Interestingly, estimating p from a random sample of coin tosses is equivalent to estimating errorv(h) from testing h on a random sample of instances A single 3 toss of the coin corresponds to drawing a single random instance from 2 and determining whether it is misclassified by h The probability p that a single random coin toss will turn up heads corresponds to the probability that a single instance drawn at random will be misclassified (ie, p corresponds to errorv(h)) The number r of heads observed over a sample of n coin tosses corresponds to the number of misclassifications observed over n randomly drawn instances Thus rln corresponds to errors(h) The problem of estimating p for coins is identical to the problem of estimating errorv(h) for hypotheses The Binomial distribution gives the general form of the probability distribution for the random variable r, whether it represents the number of heads in n coin tosses or the number of hypothesis errors in a sample of n examples The detailed form of the Binomial distribution depends on the specific sample size n and the specific probability p or errorv(h) The general setting to which the Binomial distribution applies is: 1 There is a base, or underlying, experiment (eg, toss of the coin) whose outcome can be described by a random variable, say Y The random variable Y can take on two possible values (eg, Y = 1 if heads, Y = 0 if tails) 2 The probability that Y = 1 on any single trial of the underlying experiment is given by some constant p, independent of the outcome of any other experiment The probability that Y = 0 is therefore (1 - p) Typically, p is not known in advance, and the problem is to estimate it 3 A series of n independent trials of the underlying experiment is performed (eg, n independent coin tosses), producing the sequence of independent, identically distributed random variables Y l , Yz, , Yn Let R denote the number of trials for which Yi= 1 in this series of n experiments.

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Jan 4, 2018 · 本文介绍在C#中使用ThoughtWorks.QRCode.dll生成指定尺寸和边框宽度的二维码。网上文章大多只是简单介绍内置参数的设置,根据我的使用 ...

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Nov 1, 2017 · In this article you will learn how to generate QR Code in C#. ... Create a Windows Form Application project in VS, add the dll to your .

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Nov 7, 2018 · How to Generate QR Code using QRCoder in C# Windows Forms Application QRCoder is a ...Duration: 4:41 Posted: Nov 7, 2018

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4 The probability that the random variable R will take on a specific value r (eg, the probability of observing exactly r heads) is given by the Binomial distribution n! Pr(R = r ) = pr(l - p)"-' r!(n - r ) ! A plot of this probability distribution is shown in Table 53

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