Regulariser
Particle
- class stonesoup.regulariser.particle.MCMCRegulariser(transition_model: TransitionModel = None)[source]
Bases:
Regulariser
Markov chain Monte-Carlo (MCMC) move steps, or regularisation steps, can be implemented in particle filters to prevent sample impoverishment that results from resampling. One way of avoiding this is to only perform resampling when deemed necessary by some measure of effectiveness. Sometimes this is not desirable, or possible, when a particular algorithm requires the introduction of new samples as part of the filtering process for example.
This is a particular implementation of a MCMC move step that uses the Metropolis-Hastings algorithm [1]. After resampling, particles are moved a small amount, according do a Gaussian kernel, to a new state only if the Metropolis-Hastings acceptance probability is met by a random number assigned to each particle from a uniform random distribution, otherwise they remain the same. Further details on the implementation are given in [2].
References
- Parameters:
transition_model (
TransitionModel
, optional) – Transition model used for prediction
- transition_model: TransitionModel
Transition model used for prediction
- regularise(prior, posterior)[source]
Regularise the particles
- Parameters:
prior (
ParticleState
type) – prior particle distribution.posterior (
ParticleState
type) – posterior particle distribution.
- Returns:
particle state – The particle state after regularisation
- Return type: