A simple implementation of Hamiltonian Monte Carlo in JAX

This is a simple implementation of Hamiltonian Monte Carlo in JAX that is vectorized and supports pytree parameters (i.e. tree-like structures).

Here’s a minimal example to sample from a distribution:

import jax

import jax.numpy as jnp

from hmc import hmc_sampler
# define target distribution

def target_log_pdf(params):

    return jax.scipy.stats.t.logpdf(params, df=1).sum()
# run HMC

params_init = jnp.zeros(10)
 
 

 
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