## Tuesday, July 2, 2013

### log-math: Dirichlet Distribution

A long time since my last post again. But I am working on one for Gaussian Processes but I am procrastinate there. So I decided to write another one.

As my series on Expectation Maximisation ("Thoughts on EM") evolved from research, implementations and readings, I will start a new series posting probability functions in their log likelihood form. So in the first post I will post my "log calculations" for the Dirichlet Distribution.
The Dirichlet Distribution is often used as a prior on a multinomial distribution. Using that prior one can  estimate Multinomial Distributions more stable, meaning your probability estimates do not go wild. In simple words the Dirichlet Distribution is a distribution over a Multinomial Distribution with parameters
alpha. The alpha parameter is a vector of the same size as the Multinomial Distribution. The probability
density function is:

The scaler can be expressed using the beta function:

The gamma is well the gamma function,  a numerical stable log implementation is available in the submission: "Algorithm 291: logarithm of gamma function". So let us start by converting the actual distribution to log:

Converting the scaler is also straight forwards:

And that is actually all already :D. The log - gamma function can be implemented from the paper :D