bayesian_optimiser

bojaxns.gaussian_process_formulation.bayesian_optimiser

Module Contents

tfpb

class BayesianOptimiser(experiment, num_parallel_solvers=1, beta=0.5, S=512)

Parameters:

• experiment (bojaxns.experiment.OptimisationExperiment) –

• num_parallel_solvers (int) –

• beta (float) –

• S (int) –

posterior_solve(key)

Parameters:

key (chex.PRNGKey) –

Return type:

jaxns.internals.types.NestedSamplerResults

search_U_top1(key, ns_results, batch_size, num_search)

Parameters:

• key (chex.PRNGKey) –

• ns_results (bojaxns.base.MarginalisationData) –

• batch_size (int) –

• num_search (int) –

search_U_top2(key, ns_results, u1, batch_size, num_search)

Parameters:

• key (chex.PRNGKey) –

• ns_results (bojaxns.base.MarginalisationData) –

• u1 (jax.numpy.ndarray) –

• batch_size (int) –

• num_search (int) –

choose_next_U_toptwo(key, batch_size, num_search)

Parameters:

• key (chex.PRNGKey) –

• batch_size (int) –

• num_search (int) –

choose_next_U_multistep(key, batch_size, max_depth, num_simulations, branch_factor)

Parameters:

• key (chex.PRNGKey) –

• batch_size (int) –

• max_depth (int) –

• num_simulations (int) –

• branch_factor (int) –