vSMC: Parallel Sequential Monte Carlo in C++. Sequential Monte Carlo is a family of algorithms for sampling from a sequence of distributions. Some of these algorithms, such as particle filters, are widely used in the physics and signal processing researches. More recent developments have established their application in more general inference problems such as Bayesian modeling. These algorithms have attracted considerable attentions in recent years as they admit natural and scalable parallelizations. However, these algorithms are perceived to be difficult to implement. In addition, parallel programming is often unfamiliar to many researchers though conceptually appealing, especially for sequential Monte Carlo related fields. A C++ template library is presented for the purpose of implementing general sequential Monte Carlo algorithms on parallel hardware. Two examples are presented: a simple particle filter and a classic Bayesian modeling problem.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH
- Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek: Sequential Monte Carlo Methods in the nimble R Package (2017) arXiv
- Somersalo, Erkki; Calvetti, Daniela; Arnold, Andrea: Vectorized and parallel particle filter SMC parameter estimation for stiff ODEs (2015)
- Adrien Todeschini, Francois Caron, Marc Fuentes, Pierrick Legrand, Pierre Del Moral: Biips: Software for Bayesian Inference with Interacting Particle Systems (2014) arXiv
- Zhou Y: vSMC: Parallel Sequential Monte Carlo in C++ (2013) arXiv