pymc.smc.smc.SMC_KERNEL#
- class pymc.smc.smc.SMC_KERNEL(draws=2000, start=None, model=None, random_seed=None, threshold=0.5)[source]#
Base class for the Sequential Monte Carlo kernels
To creat 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. This method is only called if start is not specified.
- _initialize_kernel: default
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 model.inial_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_kernel: optional
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_weights: default
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
- resample: default
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, 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
- tune: optional
May include logic that should be performed before every mutation step
- mutate: REQUIRED
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_stats: default
Returns important sampling_stats at the end of each stage in a dictionary format. This will be saved in the final InferenceData objcet under sample_stats.
Finally, at the end of sampling the following methods are called:
- _posterior_to_trace: default
Convert final population of particles to a posterior trace object. This method should not be modified.
- sample_settings: default:
Returns important sample_settings at the end of sampling in a dictionary format. This will be saved in the final InferenceData objcet under sample_stats.
Methods
SMC_KERNEL.__init__([draws, start, model, ...])- Parameters
Create an initial population from the prior distribution
Apply kernel-specific perturbation to the particles once per stage
Resample particles based on importance weights
Kernel settings to be saved once at the end of sampling
Stats to be saved at the end of each stage
Setup logic performed once before sampling starts
Tuning logic performed before every mutation step
Calculate the next inverse temperature (beta)