AeHMC provides MCMC sampling algorithms written in Aesara.

Features

• Sample from an (unnormalized) probability distribution using Hamiltonian Monte Carlo and the No U-Turn Sampler.

Example

import aesara
from aesara import tensor as at
from aesara.tensor.random.utils import RandomStream

from aeppl import joint_logprob

from aehmc import nuts

# A simple normal distribution
Y_rv = at.random.normal(0, 1)


def logprob_fn(y):
    return joint_logprob({Y_rv: y})


# Build the transition kernel
srng = RandomStream(seed=0)
kernel = nuts.kernel(srng, logprob_fn)

# Compile a function that updates the chain
y_vv = Y_rv.clone()
initial_state = nuts.new_state(y_vv, logprob_fn)

step_size = at.as_tensor(1e-2)
inverse_mass_matrix=at.as_tensor(1.0)
(
    next_state,
    potential_energy,
    potential_energy_grad,
    acceptance_prob,
    num_doublings,
    is_turning,
    is_diverging,
), updates = kernel(*initial_state, step_size, inverse_mass_matrix)

next_step_fn = aesara.function([y_vv], next_state, updates=updates)

print(next_step_fn(0))
# 0.14344008534533775

Installation

The latest release of AeHMC can be installed from PyPI using pip:

pip install aehmc

Or via conda-forge:

conda install -c conda-forge aehmc

The current development branch of AeHMC can be installed from GitHub using pip:

pip install git+https://github.com/aesara-devs/aehmc