pymc.smc.smc.MH#

class pymc.smc.smc.MH(*args, correlation_threshold=0.01, **kwargs)[source]#

Metropolis-Hastings SMC kernel

Methods

MH.__init__(*args[, correlation_threshold])

Parameters

MH.initialize_population()

Create an initial population from the prior distribution

MH.mutate()

Metropolis-Hastings perturbation.

MH.resample()

Resample particles based on importance weights

MH.sample_settings()

Kernel settings to be saved once at the end of sampling

MH.sample_stats()

Stats to be saved at the end of each stage

MH.setup_kernel()

Proposal dist is just a Multivariate Normal with unit identity covariance.

MH.tune()

Update proposal scales for each particle dimension and update number of MH steps

MH.update_beta_and_weights()

Calculate the next inverse temperature (beta)