Particle filter-based Gaussian process optimisation for parameter inference
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README.md
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README.md

gpo-ifac2014

Particle Bayesian optimisation for parameter inference in nonlinear state space models

This code was downloaded from < https://github.com/compops/gpo-ifac2014 > and contains the code used to produce some of the results in

J. Dahlin and F. Lindsten, Particle filter-based Gaussian Process Optimisation for Parameter Inference. In Proceedings of the 18th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August 2014.

A pre-print of the paper is found at < http://arxiv.org/abs/1311.0689 >.

Requirements

The program is written in Matlab 2013a and uses the GPML toolbox for the Gaussian process modelling. The GMPL toolbox is available for download from http://www.gaussianprocess.org/gpml/code/matlab/doc/. The program also requires a DIRECT optimisation algorithm, available for download at < http://www4.ncsu.edu/~ctk/Finkel_Direct/ >.

Included files

RUNME Executes the Matlab program that reproduces the plot in the paper for the Stochastic volaility model.

pf Runs a bootstrap particle filter with systematic resampling to estimate the log-likelihood.

datagen Generates data from a general state space model given function handles and the input.

evalMu Evaluates the posterior mean for the Gaussian processes (used by Direct)

EI Evaluates the expected improvment by using the Gaussian processes (used by Direct)