Evaluating retrieval-augmented generation (RAG) is easier than ever, but you need to keep a close eye on the LLMs that drive the evaluation metrics. We discuss some common pitfalls and solutions.

Use monad-bayes and rhine in your interactive machine learning application

Tweag releases the full source code of the Chainsail web service, for sampling multimodal distributions, first announced in August 2022. This blog post gives a tour of the Chainsail service architecture, links out to the relevant parts of the source code and proposes possible extensions to Chainsail for which the Tweag team would welcome contributions from the community.

Tweag intern Abdellatif summarizes his internship, in which he augmented Chainsail with a Bayesian Replica Exchange scheme to improve sampling of multimodal distributions.

My experience improving monad-bayes, the probabilistic programming language package, as a Tweag fellow.

We recently released a beta version of a web service called Chainsail, that helps with sampling multimodal probability distributions (you can check out our Chainsail announcement blog post for a quick introduction to this tool). In parallel, this post aims to illustrate a use…

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.

How to get reproducible development environments for probabilistic programming packages such as PyMC3, Theano or TensorFlow using Nix.

A discussion and benchmark of an alternative integrator for Hamiltonian Monte Carlo.

Apostolos Chalkis introduces dingo, a package for the analysis of metabolic networks, on which he worked as part of this Tweag fellowship.

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.

Learn about Hamiltonian Monte Carlo, and how to implement it from scratch.

In this blog post series, we're going to lead you through Bayesian modeling in Haskell with the monad-bayes library. In the third part of the series, we setup a simple Bayesian neural network.

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.

Here's Part 2 in Tweag's Series about Bayesian modeling in Haskell with the monad-bayes library.

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.

In this blog post series, we're going to lead you through Bayesian modeling in Haskell with the monad-bayes library. In the first part of the series, we introduce two fundamental concepts of `monad-bayes`: `sampling` and `scoring`.