LittleMCMC is a lightweight, performant implementation of Hamiltonian Monte Carlo (HMC) and the No-U-Turn Sampler (NUTS) in Python. This document aims to explain and contextualize the motivation and purpose of LittleMCMC. For an introduction to the user-facing API, refer to the quickstart tutorial.
Motivation and Purpose¶
Bayesian inference and probabilistic computation is complicated and has many moving parts_. As a result, many probabilistic programming frameworks (or any library that automates Bayesian inference) are monolithic libraries that handle everything from model specification (including automatic differentiation of the joint log probability), to inference (usually via Markov chain Monte Carlo or variational inference), to diagnosis and visualization of the inference results_. PyMC3 and Stan are two excellent examples of such monolithic frameworks.
However, such monoliths require users to buy in to entire frameworks or ecosystems. For example, a user that has specified their model in one framework but who now wishes to migrate to another library (to take advantage of certain better-supported features, say) must now reimplement their models from scratch in the new framework.
LittleMCMC remedies this exact use case: by isolating PyMC’s HMC/NUTS code in a standalone library, users with their own log probability function and its derivative may use PyMC’s battle-tested HMC/NUTS samplers.
LittleMCMC in Context¶
LittleMCMC stands on the shoulders of giants (that is, giant open source projects). Most obviously, LittleMCMC builds from (or, more accurately, is a spin-off project from) the PyMC project (both PyMC3 and PyMC4).
In terms of prior art, LittleMCMC is similar to several other open-source libraries, such as NUTS by Morgan Fouesneau or Sampyl by Mat Leonard. However, these libraries do not offer the same functionality as LittleMCMC: for example, they do not allow for easy changes of the mass matrix (instead assuming that an identity mass matrix), or they do not raise sampler errors or track sampler statistics such as divergences, energy, etc.
By offering step methods, integrators, quadpotentials and the sampling loop in separate Python modules, LittleMCMC offers not just a battle-tested sampler, but also an extensible one: users may configure the samplers as they wish.