Discover compops/lic-thesis Open Source project

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Created over 10 years ago
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Source code and data for examples in thesis "Sequential Monte Carlo for inference in nonlinear state space models"

pmh-tutorial

Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"

gpo-smc-abc

Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods

Python

gpo-ifac2014

Particle filter-based Gaussian process optimisation for parameter inference

MATLAB

phd-thesis

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

Python

barx-sysid2018

Sparse Bayesian ARX models with flexible noise distributions

pmh-tutorial-rpkg

R package pmhtutorial available from CRAN.

pmmh-correlated2015

Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables

Python

rjmcmc-sysid2012

Hierarchical Bayesian approaches for robust inference in ARX models

MATLAB

newton-sysid2015

Newton-based maximum likelihood estimation in nonlinear state space models

Python

panel-dpm2016

Approximate Bayesian inference for mixed effects models with heterogeneity

qnmh-sysid2018

Constructing Metropolis-Hastings proposals using damped BFGS updates

Python

ml-seminar-20180816

Python

pmh-stco2015

Particle Metropolis-Hastings using gradient and Hessian information

Python

ml-examples

Implementations from a graduate course following "Pattern Recognition and Machine Learning) written by Bishop and published in 2006.

MATLAB

smc-toyexample

Sequential Monte Carlo methods (particle filtering/smoothing) for a toy problem

Mercury

pmmh-qn

Python

compops/lic-thesis

compops

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