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Bayesian state-space modelling on high-performance hardware, including multicore, GPUs and distributed clusters.

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LibBi README.md

LibBi is used for state-space modelling and Bayesian inference on modern computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters.

The staple methods used in LibBi are those based on sequential Monte Carlo (SMC). This includes particle Markov chain Monte Carlo (PMCMC) and SMC^2 methods. Extra methods include the extended Kalman filter and some parameter optimisation routines.

LibBi consists of a C++ template library, as well as a parser and compiler, written in Perl, for its own domain-specific language that is used to specify models.

See the INSTALL.md file for installation instructions.

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Bayesian state-space modelling on high-performance hardware, including multicore, GPUs and distributed clusters.

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