9 August 2022
Chainsail: sampling multimodal distributions made easy
Tweag announces Chainsail, a simple-to-use web service for better sampling of multimodal distributions with a scalable and auto-tuning Replica Exchange algorithm at its core.
30 September 2021
A higher-order integrator for Hamiltonian Monte Carlo
A discussion and benchmark of an alternative integrator for Hamiltonian Monte Carlo.
28 October 2020
Markov chain Monte Carlo Sampling (4)
In the final post of Tweag's four-part series, we discuss Replica Exchange, a powerful MCMC algorithm designed to improve sampling from multimodal distributions. An illustrative example and, as always, an interactive Python notebook with easy-to-modify code lead to an intuitive understanding and invite experimentation.
6 August 2020
Markov Chain Monte Carlo Sampling (3)
Learn about Hamiltonian Monte Carlo, and how to implement it from scratch.
9 January 2020
Markov chain Monte Carlo Sampling (2)
In this second post of Tweag's four-part series, we discuss Gibbs sampling, an important MCMC-related algorithm which can be advantageous when sampling from multivariate distributions. Two different examples and, again, an interactive Python notebook illustrate use cases and the issue of heavily correlated samples.
25 October 2019
Markov chain Monte Carlo Sampling (1)
In this first post of Tweag's four-part series on Markov chain Monte Carlo sampling algorithms, you will learn about why and when to use them and the theoretical underpinnings of this powerful class of sampling methods. We discuss the famous Metropolis-Hastings algorithm and give an intuition on the choice of its free parameters. Interactive Python notebooks invite you to play around with MCMC yourself and thus deepen your understanding of the Metropolis-Hastings algorithm.