minipcn 0.2.0a1

A minimal preconditioned Crank-Nicolson MCMC sampler

minipcn

A minimal implementation of preconditioned Crank-Nicolson MCMC sampling.

Installation

minipcn can be installed using from PyPI using pip:

pip install minipcn

Usage

The basic usage is:

from minipcn import Sampler
import numpy as np

log_prob_fn = ...    # Log-probability function - must be vectorized
dims = ...    # The number of dimensions
rng = np.random.default_rng(42)

sampler = Sampler(
    log_prob_fn=log_prob_fn,
    dims=dims,
    step_fn="pcn",    # Or tpcn
    rng=rng,
)

# Generate initial samples
x0 = rng.randn(size=(100, dims))

# Run the sampler
chain, history = sampler.run(x0, n_steps=500)

For a complete example, see the examples directory.

Support for array-api

minipcn also supports different array API backends via array-api-compat and orng for random number generation. These can be installed by running

pip install minicpn[array-api]

Usage is then similar to when using numpy, except one must use the RNG from orng and specify the backend via xp:

from minipcn import Sampler
from orng import ArrayRNG
import torch

log_prob_fn = ...    # Log-probability function - must be vectorized
dims = ...    # The number of dimensions
rng = ArrayRNG(backend="torch", seed=42)

sampler = Sampler(
    log_prob_fn=log_prob_fn,
    dims=dims,
    step_fn="pcn",    # Or tpcn
    rng=rng,
    xp=torch,
)

# Generate initial samples
x0 = rng.randn(size=(100, dims))

# Run the sampler
chain, history = sampler.run(x0, n_steps=500)

Note: this still uses numpy when initializing the tpcn kernel.

Citing minipcn

If you use minipcn in your work, please cite our DOI

If using the tpcn kernel, please also cite Grumitt et al