Introduction

This is a library to assist with calculations for estimation problems using particle based methods, it contains a number of algorithms such as the Particle Filter, Auxiliary Particle Filter and support several variants of Particle Smoothing through the use of Backward Simulation (FFBSi) techniques but also methods such as the Metropolis-Hastings Backward Proposer (MHBP) and the Metropolis-Hastings Improved Particle Smoother (MHIPS).

It also provides a framework for doing parameter estimation in nonlinear models using Expectation Maximization combined with the particle smoothing algorithms presented above. (PS-EM).

The use of Rao-Blackwellized models is considered an importan special case and extensive support for it is provided.

The structure is based on presenting a number of interfaces that a problem specific class must implement in order to use the algorithms. To assisst the end user base classes for common model structures, such as Mixed Linear/Nonlinear Gaussian (MLNLG) models are provided to keep the implementation effort to a minimum.

The idea is to provide an easy prototyping enviroment for testing different algorithms and model formulations when solving a problem and to act as a stepping stone for a later more performance oriented problem specific implementation by the end user. (outside the scope of this framework)

There are three main areas of interest for the end user, where extra focus has been spent writing clear docstring explaining the expected usage and behavior

1. The Simulator class in the pyparticleest.simulator module is the main entry point for the application, it is used for running the different algorithms and accessing the results.

2. The abstract base classes in pyparticleest.interfaces, they define which operations that needs to be implemented when using different classes of algorithms.

3. pyparticleest.models, this modue contains support code for common model classes, currently: Nonlinar Gaussian (NLG), Mixed Linear/Nonlinear Gaussian (MLNLG), Hierarchical, Linear Time-Varying (LTV)

You are encourage to study the examples in the documentation, more can be under test/manual in the source code. They demonstrate how the framework is instended to be used.

A good starting point is

test/manual/filtering/basic_model.py

followed by

test/manual/filtering/nonlin_model.py

For an example of a simple MLNLG model see

test/manual/smoothing/mlnlg_model.py

and for the commonly used “standard nonlinear model” see

test/manual/smoothing/standard_nonlin_model.py