compops/pmh-stco2015
Particle Metropolis-Hastings using gradient and Hessian information
Source code and data for examples in thesis "Sequential Monte Carlo for inference in nonlinear state space models"
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Particle Metropolis-Hastings using gradient and Hessian information
Particle filter-based Gaussian process optimisation for parameter inference
Implementations from a graduate course following "Pattern Recognition and Machine Learning) written by Bishop and published in 2006.
Sequential Monte Carlo methods (particle filtering/smoothing) for a toy problem