SMCTC: Sequential Monte Carlo in C++. Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH
- Bouchard-Côté, Alexandre; Doucet, Arnaud; Roth, Andrew: Particle Gibbs split-merge sampling for Bayesian inference in mixture models (2017)
- Adrien Todeschini, Francois Caron, Marc Fuentes, Pierrick Legrand, Pierre Del Moral: Biips: Software for Bayesian Inference with Interacting Particle Systems (2014) arXiv
- Lawrence M. Murray: Bayesian State-Space Modelling on High-Performance Hardware Using LibBi (2013) arXiv
- Zhou Y: vSMC: Parallel Sequential Monte Carlo in C++ (2013) arXiv
- Adam Johansen: SMCTC: Sequential Monte Carlo in C++ (2009) not zbMATH