bayes-kit

bayes-kit is an open-source Python package for Bayesian inference and posterior analysis with minimial dependencies for maximal flexiblity.

Example

The following example defines a model StdNormal, samples 1000 draws using Metropolis-adjusted Langevin sampling, and prints the mean and variance estimates.

import numpy as np
from bayes_kit.mala import MALA

class StdNormal:
	def dims(self):
		return 1
	def log_density(self, theta):
		return -0.5 * theta[0]**2
	def log_density_gradient(self, theta):
		return self.log_density(theta), -theta
	
model = StdNormal()
sampler = MALA(model, 0.2)
M = 1000
draws = np.array([sampler.sample()[0] for _ in range(M)])

print(f"{draws.mean()=}  {draws.var()=}")
print(f"{draws[0:5]=}")

Model specification

For bayes-kit, a Bayesian model is specified as a class implementing a log density function with optional gradients and Hessians. Models may be defined using arbitrary Python code, including

direct implementation in NumPy,
Python's foreign function interface to Fortran, C++, etc., or
automatic differentiation libraries such as
- Stan,
- PyTorch,
- JAX, or
- TensorFlow Probability.

Bayesian inference

Bayesian inference involves conditioning on observed data and averaging over uncertainty. Specifically, bayes-kit can compute * parameter estimates, * posterior predictions for new observations, and * event probability forecasts, all with Bayesian uncertainty quantification.

Algorithms in bayes-kit rely only on a model's log density function and optionally its derivatives, not on any particular model structure. As such, they are closed-box algorithms that treat models as encapsulated.

High performance algorithms that scale well with dimension require gradients and algorithms that adapt to varying curvature require Hessians.

Markov chain Monte Carlo samplers

Markov chain Monte Carlo (MCMC) samplers provide a sequence of random draws from target log density, which may be used for Monte Carlo estimates of posterior expectations and quantiles for uncertainty quantification.

Random-walk Metropolis sampler

Random-walk Metropolis (RWM) is a diffusive sampler that requires a target log density function and a symmetric pseudorandom proposal generator.

Metropolis-adjusted Langevin sampler

Metroplis-adjusted Langevin (MALA) is a diffusive sampler that adjusts proposals with gradient-based information. MALA requires a target log density and gradient function.

Hamiltonian Monte Carlo sampler

Hamiltonian Monte Carlo (HMC) simulates Hamiltonian dynamics with a potential energy function equal to the negative log density. It requires a target log density, gradient function, and optionally a metric.

Sequential Monte Carlo samplers

Sequential Monte Carlo (SMC) samplers alternate proposing moves and sampling importance resampling for a sequence of intermediate densities that end in the target density.

Likelihood annealed sequential Monte Carlo sampler

In this approach to SMC, the target at step n is proportional is proportional to

p(theta | y)^t[n] * p(theta),

where the temperature t[n] runs from 0 to 1 across iterations.

Dependencies

bayes-kit only depends on a single external package,

NumPy.

Licensing

Code in this repository is released under the MIT License.
Documentation in this repository is released under the CC BY 4.0 License.

jsoules / bayes-kit