Bayesian Inference Tools in Python

Our goal is, given the discrete outcomes of events, estimate the distribution of categories. Using gradient descent we can estimate the parameters of a dirchlet prior from past data that can be combined as a conjugate prior with the multinomial distribution to better estimate the likelihood of seeing an event of a given type in the future.

Conjugate Prior Tools: The main file is ./findDirichletPrior - you pipe in your counts (given in test.csv as an example) and the maximum-likelihood dirichlet comes out.

Some things to try on your terminal: cat test.csv | ./findDirichletPrior.py -- This will find the priors for a test file

./flipCoins .7 1.2 | ./findDirichletPrior.py -- This will generate a data set on the fly using dirichlet parameters .7 1.2 (feel free to change those) -- findDirichletPrior should come up with a good estimate of those numbers using only the coin flips

cat oneDoublesided.csv | ./findDirichletPrior.py -- This is a sample of a case where findDirichletPrior won't give you a great result. This is because every -- coin in the input is fair except two coins: one is double sided heads, and the other tails. -- Dirichlet distributions cannot handle this trimodal data very well, but it'll end up giving a compromise solution

#Using the priors You can test the strength of your prior using the H parameter. Higher values for Beta will give lower probabilities.

python findDirichletPrior.py -H1,4,5 < /dev/null

gammaDistTools is not used. These functions will be used for a future gamma distribution estimations.