Bayesian state-space modelling on high-performance hardware, including multicore, GPUs and distributed clusters.
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
docs
eclipse
inc/Module
lib
script
share
t
.gitignore
.includepath
.project
Doxyfile
INSTALL.md
INSTALL_GENERIC.md
INSTALL_LINUX.md
INSTALL_MAC.md
INSTALL_WIN.md
LICENSE
MANIFEST
MANIFEST.SKIP
MakeDocs.PL
MakeManifest.PL
MakeParser.PL
Makefile.PL
README.md
Test.bi
VERSION.md
test.conf

README.md

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.