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LibBi (library for Bayesian inference)

LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC. Other methods include the extended Kalman filter and some parameter optimisation routines. Easily install LibBi from Homebrew.

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LibBi (library for Bayesian inference) | libbi.org Reviews

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LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC. Other methods include the extended Kalman filter and some parameter optimisation routines. Easily install LibBi from Homebrew.

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LibBi (library for Bayesian inference) | libbi.org Reviews

https://libbi.org

LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC. Other methods include the extended Kalman filter and some parameter optimisation routines. Easily install LibBi from Homebrew.

INTERNAL PAGES

libbi.org

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New LibBi mailing list

http://libbi.org/2013/11/29/LibBi-Mailing-List.html

New LibBi mailing list. On 29 Nov 2013. A mailing list has been set up for LibBi users over at Google Groups. Please subscribe to the list if you are interested in LibBi, and post there for help with installing or using it. LibBi has been developed under a CSIRO. Project that forms part of the Computational and Simulation Sciences. Website theme adapted from work by orderedlist.

2

Four new packages for diffusion bridge sampling

http://libbi.org/2014/05/19/Four-new-packages-for-diffusion-bridge-sampling.html

Four new packages for diffusion bridge sampling. On 19 May 2014. Four new packages are now available to demonstrate the bridge particle filter included in LibBi 1.1.0. The bridge particle filter is described in this new paper:. Del Moral, P. and Murray, L. M. Sequential Monte Carlo with highly informative Observations, 2014. [arXiv]. The bridge particle filter is enabled in LibBi by adding a. Command-line option, and a. Option to set the frequency of intermediate times. A. The four packages are:. These f...

3

Updated paper and package on resampling algorithms in LibBi

http://libbi.org/2014/05/08/Updated-paper-and-package-on-resampling-algorithms-in-LibBi.html

Updated paper and package on resampling algorithms in LibBi. On 08 May 2014. The following paper has been updated on arXiv, detailing the particle filter resampling schemes implemented in LibBi, including the Metropolis and rejection algorithms, which can provide an extra performance boost:. L M Murray, A. Lee, and P. E. Jacob. Parallel resampling in the particle filter. Package has also been added to the website. It can be used to reproduce the results of that paper using the.

4

LibBi Released

http://libbi.org/2013/06/07/LibBi-Released.html

On 07 Jun 2013. The initial release of LibBi was made today. LibBi has been developed under a CSIRO. Project that forms part of the Computational and Simulation Sciences. Website theme adapted from work by orderedlist.

5

LibBi: Related projects

http://libbi.org/related.html

OctBi is a package for GNU Octave. With functions for plotting the outputs of LibBi. It is not a complete environment for calling LibBi from within Octave, however. OctBi should also work with MATLAB, but has not been thoroughly tested with it as yet. RBi is a work in progress to create a package for R. With similar functionality to OctBi. It has limited functionality at this stage. LibBi has been developed under a CSIRO. Project that forms part of the Computational and Simulation Sciences.

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Dominic Steinitz | Maths, Stats & Functional Programming

https://idontgetoutmuch.wordpress.com/author/idontgetoutmuch

Maths, Stats and Functional Programming. Formerly known as Idontgetoutmuch's Weblog. Modelling an Ecosystem via Hamiltonian Monte Carlo. July 4, 2016. Recall from the previous post. That the Hare growth parameter undergoes Brownian motion so that the further into the future we go, the less certain we are about it. In order to ensure that this parameter remains positive, let’s model the log of it to be Brownian motion. Where the final equation is a stochastic differential equation with. By Itô we have.

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libbi.org

LibBi (library for Bayesian inference)

LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC. Other methods include the extended Kalman filter and some parameter optimisation routines. Easily install LibBi from Homebrew.

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