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Bayesian State-Space Modelling on High-Performance Hardware Using LibBi

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If you have a question about this talk, please contact Sumeetpal Sidhu Singh .

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. The focus of the software 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. This talk will serve as an introduction to the software, and will include a selection of example applications drawn from the environmental sciences.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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