ParticleFilter¶

class probnum.filtsmooth.particle.ParticleFilter(prior_process, importance_distribution, num_particles, rng, with_resampling=True, resampling_percentage_threshold=0.1)

Particle filter (PF). Also known as sequential Monte Carlo method.

A PF estimates the posterior distribution of a Markov process given noisy, non-linear observations, with a set of particles.

The random state of the particle filter is inferred from the random state of the initial random variable.

Parameters

Methods Summary

 filter(regression_problem) Apply particle filtering to a data set. filter_generator(regression_problem) Apply Particle filtering to a data set.

Methods Documentation

filter(regression_problem)[source]

Apply particle filtering to a data set.

Parameters

regression_problem (TimeSeriesRegressionProblem) – Regression problem.

Returns

• posterior – Posterior distribution of the filtered output

• info_dicts – list of dictionaries containing filtering information

See also

TimeSeriesRegressionProblem

a regression problem data class

filter_generator(regression_problem)[source]

Apply Particle filtering to a data set.

Parameters

regression_problem (TimeSeriesRegressionProblem) – Regression problem.

Raises

ValueError – If repeating time-points are encountered.

Yields
• curr_rv – Filtering random variable at each grid point.

• info_dict – Dictionary containing filtering information

See also

TimeSeriesRegressionProblem

a regression problem data class