# Hamiltonian MCMC Sampler¶

class sampyl.Hamiltonian(logp, start, step_size=1, n_steps=5, **kwargs)
sample(num, burn=0, thin=1, n_chains=1, progress_bar=True)

Sample from $$P(X)$$

Parameters: num – int. Number of samples to draw from $$P(X)$$. burn – (optional) int. Number of samples to discard from the beginning of the chain. thin – (optional) float. Thin the samples by this factor. n_chains – (optional) int. Number of chains to return. Each chain is given its own process and the OS decides how to distribute the processes. progress_bar – (optional) boolean. Show the progress bar, default = True. Record array with fields taken from arguments of logp function.
step()

This is what you define to create the sampler. Requires that a state object is returned.