When I give talks about probabilistic programming and Bayesian statistics, I usually gloss over the details of how inference is actually performed, treating it as a black box essentially. The beauty of probabilistic programming is that you actually don't have to understand how the inference works in order to build …
Python has an extremely rich and healthy ecosystem of data science tools. Unfortunately, to outsiders this ecosystem can look like a jungle (cue snake joke). In this blog post I will provide a step-by-step guide to venturing into this PyData jungle.
What's wrong with the many lists of PyData packages …
Today's blog post is co-written by my student Danne Elbers who is doing her masters thesis with me on computational psychiatry using Bayesian methods. This post also borrows heavily from a Notebook by Chris Fonnesbeck.
The power of Bayesian modelling …
Have you ever asked yourself: "Do I want to spend 2 days adjusting this analysis to run on the cluster and wait 2 days for the jobs to finish or do I just run it locally with no extra work and just wait a week."
If so, this blog post …
Here's the deal: I used PyMC3, matplotlib, and Jake Vanderplas' JSAnimation to create javascript animations of three MCMC sampling algorithms -- Metropolis-Hastings, slice sampling and NUTS.
I like visualizations because they provide a good intuition for how the samplers work and what problems they can run into.
You can download the …
tl;dr: I hacked the emcee--The MCMC-Hammer ensemble sampler to work on PyMC models.
PyMC is an awesome Python module to perform Bayesian inference. It allows for flexible model creation and has basic MCMC samplers like Metropolis-Hastings. The upcoming PyMC3 will feature much fancier samplers like Hamiltonian-Monte Carlo …
Author: Thomas Wiecki
This tutorial first appeard as a post in small series on Bayesian GLMs on my blog:
Author: Thomas Wiecki
This tutorial appeared as a post in a small series on Bayesian GLMs on my blog: