- class pymc.smc.kernels.MH(*args, correlation_threshold=0.01, **kwargs)[source]#
Create an initial population from the prior distribution
Resample particles based on importance weights
SMC_kernel settings to be saved once at the end of sampling.
Stats to be saved at the end of each stage
Proposal dist is just a Multivariate Normal with unit identity covariance.
Update proposal scales for each particle dimension and update number of MH steps
Calculate the next inverse temperature (beta)