LibBi
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi. LibBi is a software package for state-space modelling and Bayesian inference on modern computer hardware, including multi-core central processing units (CPUs), many-core graphics processing units (GPUs) and distributed-memory clusters of such devices. The software parses a domain-specific language for model specification, then optimises, generates, compiles and runs code for the given model, inference method and hardware platform. In presenting the software, this work serves as an introduction to state-space models and the specialised methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo (SMC) methods such as the particle filter for state estimation, and the particle Markov chain Monte Carlo (PMCMC) and SMC^2 methods for parameter estimation. All are well-suited to current computer hardware. Two examples are given and developed throughout, one a linear three-element windkessel model of the human arterial system, the other a nonlinear Lorenz ’96 model. These are specified in the prescribed modelling language, and LibBi demonstrated by performing inference with them. Empirical results are presented, including a performance comparison of the software with different hardware configurations.
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References in zbMATH (referenced in 13 articles , 1 standard article )
Showing results 1 to 13 of 13.
Sorted by year (- Johan Dahlin, Thomas B. Schön: Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models (2019) not zbMATH
- Law, Jonathan; Wilkinson, Darren J.: Composable models for online Bayesian analysis of streaming data (2018)
- Jerker Nordh: pyParticleEst: A Python Framework for Particle-Based Estimation Methods (2017) not zbMATH
- Mingas, Grigorios; Bottolo, Leonardo; Bouganis, Christos-Savvas: Particle MCMC algorithms and architectures for accelerating inference in state-space models (2017)
- Nicholas Michaud, Perry de Valpine, Daniel Turek, Christopher J. Paciorek: Sequential Monte Carlo Methods in the nimble R Package (2017) arXiv
- Picchini, Umberto; Forman, Julie Lyng: Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation (2016)
- Del Moral, Pierre; Murray, Lawrence M.: Sequential Monte Carlo with highly informative observations (2015)
- Jacob, Pierre E.: Sequential Bayesian inference for implicit hidden Markov models and current limitations (2015)
- Jacob, Pierre E.; Murray, Lawrence M.; Rubenthaler, Sylvain: Path storage in the particle filter (2015)
- Somersalo, Erkki; Calvetti, Daniela; Arnold, Andrea: Vectorized and parallel particle filter SMC parameter estimation for stiff ODEs (2015)
- Targino, Rodrigo S.; Peters, Gareth W.; Shevchenko, Pavel V.: Sequential Monte Carlo samplers for capital allocation under copula-dependent risk models (2015)
- 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