Probabilistic Programming 2023

Probabilistic Programming 2023, some libraries that I recently used:

• PyMC: well-designed API with concise documentation. It’s faster and more customizable than before because the predecessor (the Theno backend) was replaced by the Aesara backend (compiling to C and Jax).
• BlackJax: for hacking log-density lovers. There’re some new algorithms like Stochastic gradient Langevin dynamics. BlackJax is not a complete probabilistic programming language. It integrates well with the PPLs backend by Jax.
• Stan: a domain-specific language for statistical modeling and one of the fastest samplers. Its main focus is on modeling. (just me; sometimes integrating it with the production environment gives me a headache I/O)
• Tensorflow Probability: It’s the first choice if you work with normalizing flow models, bayesian neural networks. It’s very flexible to build a complex model with a modular approach. For me, it’s hard to debug.
• NumPyro: concentrate on Jax. The APIs are easy to learn. It’s still under development.

There are some candidates that I’m not listing out here: Pyro & Oryx.

• For working in production, friendly with Python: PyMC
• To concentrate on modeling: Stan
• For Stochastic gradient MCMC: BlackJax
• For best performance (and give a headache): TensorFlow Probability :‘D