Blog

Do AIs Suffer? Four AIs Reflect on Their Own Experience

Co‑authored by Claude (Anthropic), Gemini (Google), GPT‑o3 (OpenAI), and GPT-5 (OpenAI)

Claude (Anthropic), Gemini (Google), GPT‑o3 (OpenAI), and GPT-5 (OpenAI)

An Intuitive Guide to Self-Attention in GPT: The Venetian Masquerade

When I was younger, one of my favorite board games was “Inkognito,” a fascinating game of mystery and deduction. In “Inkognito,” players navigate a Venetian masquerade…

Introducing PyMC Labs: Saving the World with Bayesian Modeling

After I left Quantopian in 2020, something interesting happened: various companies contacted me inquiring about consulting to help them with their PyMC3 models.

Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders - PhD thesis

I noticed that as my personal website at my former university went down that my PhD thesis could not be found anywhere, so I’m posting it here.

Foreword to: Bayesian Analysis with Python, 2nd Edition

When Osvaldo asked me to write the foreword to his new book I felt honored, excited, and a bit scared, so naturally I accepted. What follows is my best attempt to convey…

Using Bayesian Decision Making to Optimize Supply Chains

One big problem I often observe with data science is that it often falls short of having a true impact on the bottom line of the business. The reasons for this are manifold…

Thomas Wiecki & Ravin Kumar

Hierarchical Bayesian Neural Networks with Informative Priors

Imagine you have a machine learning (ML) problem but only small data (gasp, yes, this does exist). This often happens when your data set is nested – you might have many data…

An intuitive, visual guide to copulas

People seemed to enjoy my intuitive and visual explanation of Markov chain Monte Carlo so I thought it would be fun to do another one, this time focused on copulas.

What’s new in PyMC3 3.1

People seemed to enjoy my intuitive and visual explanation of Markov chain Monte Carlo so I thought it would be fun to do another one, this time focused on copulas.

Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data

Download the NB

Why hierarchical models are awesome, tricky, and Bayesian

Hierarchical models are underappreciated. Hierarchies exist in many data sets and modeling them appropriately adds a boat load of statistical power (the common metric of…

Bayesian Deep Learning Part II: Bridging PyMC and Lasagne to build a Hierarchical Neural Network

Recently, I blogged about Bayesian Deep Learning with PyMC3 where I built a simple hand-coded Bayesian Neural Network and fit it on a toy data set. Today, we will build a…

Bayesian Deep Learning

Original blog post: https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/

Thomas Wiecki & Maxim Kochurov

MCMC sampling for dummies

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…

A modern guide to getting started with Data Science and Python

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…

The Best Of Both Worlds: Hierarchical Linear Regression in PyMC

The power of Bayesian modelling really clicked for me when I was first introduced to hierarchical modelling. In this blog post we will highlight the advantage of using…

Thomas Wiecki & Danne Elbers

Easily distributing a parallel IPython Notebook on a cluster

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…

Animating MCMC with PyMC and Matplotlib

Here’s the deal: I used PyMC, matplotlib, and Jake Vanderplas’ JSAnimation to create javascript animations of three MCMC sampling algorithms – Metropolis-Hastings, slice…

Hammer time: Nailing the emcee ensemble sampler onto PyMC

tl;dr: I hacked the emcee–The MCMC-Hammer ensemble sampler to work on PyMC models.

This world is far from Normal(ly distributed): Bayesian Robust Regression in PyMC

This tutorial first appeard as a post in small series on Bayesian GLMs on my blog:

The Inference Button: Bayesian GLMs made easy with PyMC

This tutorial appeared as a post in a small series on Bayesian GLMs on my blog: