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.

# Tag Archives: Bayes

# 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.