stochastic/

Boltzmann acceptance probability helpers for Metropolis MCMC <stochastic/boltzmann_table.hpp>

markov::boltzmann_accept – markov::make_boltzmann_table

Weighted categorical sampling <stochastic/categorical.hpp>

categorical_sampler – sample_categorical

Contracts for random engines, samplers, and Markov chain proposals <stochastic/concepts.hpp>

categorical_sampling – energy_difference

Template implementations for stochastic/mcmc.hpp. Included at the bottom of mcmc.hpp -- do not include directly <stochastic/detail/mcmc_impl.hpp>

markov::metropolis_sweep – markov::metropolis_sweep_prob – markov::umbrella_sweep – markov::umbrella_sweep_prob

Metropolis-Hastings sweep API <stochastic/mcmc.hpp>

markov::metropolis_stats – markov::umbrella_stats – markov::umbrella_window

Exact multinomial count sampling from nonnegative weights <stochastic/multinomial.hpp>

sample_multinomial

Random probe vectors for stochastic trace and diagonal estimation <stochastic/probe.hpp>

gaussian_probe – hutchinson_row_mean_square – rademacher_probe

The library's random engines and seeding utilities <stochastic/rng.hpp>

markov::make_rng – markov::make_seeded_rng – rng – rng64