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Stitching together probabilistic models and inference.

Project description

Bayeux

Stitching together models and samplers

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bayeux lets you write a probabilistic model in JAX and immediately have access to state-of-the-art inference methods. The API aims to be simple, self descriptive, and helpful. Simply provide a log density function (which doesn't even have to be normalized), along with a single point (specified as a pytree) where that log density is finite. Then let bayeux do the rest!

Installation

pip install bayeux-ml

Quickstart

We define a model by providing a log density in JAX. This could be defined using a probabilistic programming language (PPL) like numpyro, PyMC, TFP, distrax, oryx, coix, or directly in JAX.

import bayeux as bx
import jax

normal_density = bx.Model(
  log_density=lambda x: -x*x,
  test_point=1.)

seed = jax.random.key(0)

opt_results = normal_density.optimize.optax_adam(seed=seed)
# OR!
idata = normal_density.mcmc.numpyro_nuts(seed=seed)
# OR!
surrogate_posterior, loss = normal_density.vi.tfp_factored_surrogate_posterior(seed=seed)

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