Bayes Primer

Posted on Sat 17 October 2015 in ml • Tagged with tutorial, bayesian

What is Bayes Theorem?

Bayes theorem is what allows us to go from a sampling (or likelihood) distribution and a prior distribution to a posterior distribution.

What is a Sampling Distribution?

A sampling distribution is the probability of seeing our data (X) given our parameters ($\theta$). This is written as $p(X|\theta)$.

For example, we might have data on 1,000 coin flips. Where 1 indicates a head. This can be represented in python as

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Bayes With Continuous Prior

Posted on Fri 03 April 2015 in ml • Tagged with bayesian, tutorial

Continuous Prior

In my introduction to Bayes post, I went over a simple application of Bayes theorem to Bernoulli distributed data. In this post, I want to extend our example to use a continous prior.

In my last post, I ended with this code: