pymc.smc.kernels.SMC_KERNEL#
- class pymc.smc.kernels.SMC_KERNEL(draws=2000, start=None, model=None, random_seed=None, threshold=0.5)[source]#
Base class for the Sequential Monte Carlo kernels.
To create a new SMC kernel you should subclass from this.
Before sampling, the following methods are called once in order:
- initialize_population
Choose initial population of SMC particles. Should return a dictionary with {var.name : numpy array of size (draws, var.size)}. Defaults to sampling from the prior distribution, except for parameters which have custom initval, in which case that value is used for all SMC particles. This method is only called if start is not specified.
- _initialize_kerneldefault
Creates initial population of particles in the variable self.tempered_posterior and populates the self.var_info dictionary with information about model variables shape and size as {var.name : (var.shape, var.size)}.
The functions self.prior_logp_func and self.likelihood_logp_func are created in this step. These expect a 1D numpy array with the summed sizes of each raveled model variable (in the order specified in
pymc.Model.initial_point()).Finally, this method computes the log prior and log likelihood for the initial particles, and saves them in self.prior_logp and self.likelihood_logp.
This method should not be modified.
- setup_kerneloptional
May include any logic that should be performed before sampling starts.
During each sampling stage the following methods are called in order:
- update_beta_and_weightsdefault
The inverse temperature self.beta is updated based on the self.likelihood_logp and threshold parameter.
The importance self.weights of each particle are computed from the old and newly selected inverse temperature.
The iteration number stored in self.iteration is updated by this method.
Finally the model log_marginal_likelihood of the tempered posterior is updated from these weights.
- resampledefault
The particles in self.posterior are sampled with replacement based on self.weights, and the used resampling indexes are saved in self.resampling_indexes.
The arrays self.prior_logp and self.likelihood_logp are rearranged according to the order of the resampled particles. self.tempered_posterior_logp is computed from these and the current self.beta.
- tuneoptional
May include logic that should be performed before every mutation step.
- mutateREQUIRED
Mutate particles in self.tempered_posterior.
This method is further responsible to update the self.prior_logp, self.likelihod_logp and self.tempered_posterior_logp, corresponding to each mutated particle.
- sample_statsdefault
Returns important sampling_stats at the end of each stage in a dictionary format. This will be saved in the final InferenceData object under sample_stats.
Finally, at the end of sampling the following methods are called:
- _posterior_to_tracedefault
Convert final population of particles to a posterior trace object. This method should not be modified.
- sample_settingsdefault
Returns important sample_settings at the end of sampling in a dictionary format. This will be saved in the final InferenceData object under sample_stats.
Methods
SMC_KERNEL.__init__([draws, start, model, ...])Initialize the SMC_kernel class.
Create an initial population from the prior distribution.
Apply kernel-specific perturbation to the particles once per stage.
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.
Perform setup logic once before sampling starts.
Tuning logic performed before every mutation step.
Calculate the next inverse temperature (beta).