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Characteristic Functions Examples 1. Bernoulli Distribution he Bernoulli distribution is a discrete distribution having two possible outcomesdiscrete distribution.

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Presentation on theme: "Characteristic Functions Examples 1. Bernoulli Distribution he Bernoulli distribution is a discrete distribution having two possible outcomesdiscrete distribution."— Presentation transcript:

1 Characteristic Functions Examples 1. Bernoulli Distribution he Bernoulli distribution is a discrete distribution having two possible outcomesdiscrete distribution labelled by n=0 and n=1 in which n=1 ("success") occurs with probability p and ("failure") occurs with probability q=1-p, where 0 < p < 1. It therefore has probability function which can also be written The corresponding distribution function isdistribution function The characteristic function ischaracteristic function

2 Characteristic Function In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Such a success/failure experiment is also called a Bernoulli experiment or Bernoulli trial. In fact, when n = 1, then the binomial distribution is the Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significanceprobability theorystatisticsprobability distributionindependentprobabilityBernoulli trial Bernoulli distributionbinomial test statistical significance

3 Example A typical example is the following: assume 5% of the population is green-eyed. You pick 500 people randomly. The number of green-eyed people you pick is a random variable X which follows a binomial distribution with n = 500 and p = 0.05 (when picking the people with replacementrandom variable Probability mass function In general, if the random variable X follows the binomial distribution with parameters n and p, we write X ~ B(n, p). The probability of getting exactly k successes is given by the probability mass function:probability mass function for k=0,1,2,...,n and where

4 Parameters number of trials (integer ) success probability (real)integerreal Support Probability mass functionProbability mass function (pmf) Cumulative distribution functionCumulative distribution function (cdf) Mean Median one of Mode Variance Skewness Excess Kurtosis Entropy mgf Char. func.


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