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Discrete Categorical Distribution Discrete Categorical Distribution Carl Edward Rasmussen November 11th, 2016 Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 1 / 8 Key concepts We generalize the concepts from binary variables to multiple discrete outcomes. • discrete and multinomial distributions • the Dirichlet distribution Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 2 / 8 The multinomial distribution (1) Generalisation of the binomial distribution from 2 outcomes to m outcomes. Useful for random variables that take one of a finite set of possible outcomes. Throw a die n = 60 times, and count the observed (6 possible) outcomes. Outcome Count X = x1 = 1 k1 = 12 X = x2 = 2 k2 = 7 Note that we have one parameter too many. We X = x3 = 3 k3 = 11 don’t need to know all the ki and n, because 6 X = x4 = 4 k4 = 8 i=1 ki = n. X = x5 = 5 k5 = 9 P X = x6 = 6 k6 = 13 Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 3 / 8 The multinomial distribution (2) Consider a discrete random variable X that can take one of m values x1,..., xm. Out of n independent trials, let ki be the number of times X = xi was observed. m It follows that i=1 ki = n. m Denote by πi the probability that X = xi, with i=1 πi = 1. P > The probability of observing a vector of occurrences k = [k1,..., km] is given by P > the multinomial distribution parametrised by π = [π1,..., πm] : n! ki p(kjπ, n) = p(k1,..., kmjπ1,..., πm, n) = πi k !k !... km! 1 2 i= Y1 • Note that we can write p(kjπ) since n is redundant. n • The multinomial coefficient n! is a generalisation of . k1!k2!...km! k The discrete or categorical distribution is the generalisation of the Bernoulli to m outcomes, and the special case of the multinomial with one trial: p(X = xijπ) = πi. Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 4 / 8 Example: word counts in text Consider describing a text document by the frequency of occurrence of every distinct word. The UCI Bag of Words dataset from the University of California, Irvine. 1 1http://archive.ics.uci.edu/ml/machine-learning-databases/bag-of-words/ Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 5 / 8 Priors on multinomials: The Dirichlet distribution The Dirichlet distribution is to the categorical/multinomial what the Beta is to the Bernoulli/binomial. It is a generalisation of the Beta defined on the m - 1 dimensional simplex. > • Consider the vector π = [π1,..., πm] , with m i=1 πi = 1 and πi 2 (0, 1) 8i. • π m - PVector lives in the open standard 1 simplex. • π could for example be the parameter vector of a multinomial. [Figure on the right m = 3.] The Dirichlet distribution is given by m m m Γ( i=1 αi) αi-1 1 αi-1 Dir(πjα1,..., αm) = m πi = πi Γ(αi) B(α) i=1 i= i= P Y1 Y1 Q > • α = [α1,..., αm] are the shape parameters. • B(α) is the multivariate beta function. αj • E(πj) = m is the mean for the j-th element. i=1 αi Carl Edward RasmussenP Discrete Categorical Distribution November 11th, 2016 6 / 8 Dirichlet Distributions from Wikipedia Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 7 / 8 The symmetric Dirichlet distribution In the symmetric Dirichlet distribution all parameters are identical: αi = α, 8i. en.wikipedia.org/wiki/File:LogDirichletDensity-alpha_0.3_to_alpha_2.0.gif To sample from a symmetric Dirichlet in D dimensions with concentration α use: w = randg(alpha,D,1); bar(w/sum(w)); alpha = 0.1 alpha = 1 alpha = 10 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.5 0.5 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Distributions drawn at random from symmetric 10 dimensional Dirichlet distributions with various concentration parameters. Carl Edward Rasmussen Discrete Categorical Distribution November 11th, 2016 8 / 8.
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