Introduction To Bayesian Statistics

Kennie Nybo Pontoppidan has just completed a course on Bayesian statistics:

Last month I finished a four-week course on Bayesian statistics. I have always wondered why people deemed it hard, and why I heard that the computations quickly became complicated. The course wasn’t that hard, and it gave a nice introduction to prior/posterior distributions and I many cases also how to interpret the parameters in the prior distribution as extra data points.

An interesting aspect of Bayesian statistics is that it is a mathematically rigorous model, with no magic numbers such as the 5% threshold for p-values. And I like the way it naturally caters sequential hypothesis testing with where the sample size of each iteration is not fixed in advance. Instead data are evaluated and used to update the model as they are collected.

Check out Kennie’s explanation as well as the course.  I also went through Bayes’ Theorem not too long ago, which is a good introduction to the topic if you’re unfamiliar with Bayes’s Law.

Related Posts

Principal Component Analysis With Faces

Mic at The Beginner Programmer shows us how to creepy PCA diagrams with human faces: PCA looks for a new the reference system to describe your data. This new reference system is designed in such a way to maximize the variance of the data across the new axis. The first principal component accounts for as […]

Read More

Using Uncertainty For Model Interpretation

Yoel Zeldes and Inbar Naor explain how uncertainty can help you understand your models better: One prominent example is that of high risk applications. Let’s say you’re building a model that helps doctors decide on the preferred treatment for patients. In this case we should not only care about the accuracy of the model, but […]

Read More


September 2017
« Aug Oct »