Tag Archives: Bayes

Performing Incremental Updates using PyMC

In this post I’ll discuss how to perform incremental updates to a simple statistical model using PyMC. The short answer is that you have to create a new model each time. In this example, I’ll use a Bernoulli random variable from scipy.stats to generate coin flips, and I will use PyMC to model a prior and likelihood distribution, and produce a posterior distribution as output.

Continue reading

Modeling with Beta Distributions

The beta distribution requires two parameters, usually referred to as a and b, or alpha and beta. If you are considering a Bernoulli process, a sequence of binary outcomes (success or failure) with a constant probability of success, then you could use a beta distribution, setting the parameter a equal to the number of successes, and setting the parameter b equal to the number of failures. The neat thing about the Beta distribution is that the greater the total number of trials (the sum of the successes and failures) the more peaked, or narrow, the distribution becomes.

Continue reading