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Biips · Bayesian inference with interacting particle systems

Biips project homepage. Bayesian inference with interacting particle systems.

http://biips.github.io/

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Biips · Bayesian inference with interacting particle systems | biips.github.io Reviews

https://biips.github.io

Biips project homepage. Bayesian inference with interacting particle systems.

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1

Biips paper available on arXiv · Biips

http://biips.github.io/paper/2014/12/16/arxiv-paper.html

Middot; Bayesian inference with interacting particle systems. Biips paper available on arXiv. Dec 16, 2014.

2

Fifth IMS-ISBA joint meeting MCMSki IV · Biips

http://biips.github.io/event/2014/08/29/mcmski.html

Middot; Bayesian inference with interacting particle systems. Fifth IMS-ISBA joint meeting MCMSki IV. Aug 29, 2014. January 6-8, 2014. Chamonix Mont-Blanc, France. We will participate to a session focused on Recent Developments in Software for MCMC. Abstract of the session:. Andrew Thomas, University of Helsinki, Finland; Developer of BUGS. Martyn Plummer, International Agency for Research on Cancer, France; Developer of JAGS. Bob Carpenter, Columbia University, USA; Developer of STAN.

3

Rencontres Inria Industries Finance · Biips

http://biips.github.io/event/2012/03/29/rii-finance.html

Middot; Bayesian inference with interacting particle systems. Rencontres Inria Industries Finance. Mar 29, 2012. April 3, 2012. We will present a demonstration of Biips. Software for estimating the stochastic volatility of financial data.

4

BayesComp 2012 · Biips

http://biips.github.io/event/2012/06/09/bayescomp2012.html

Middot; Bayesian inference with interacting particle systems. Jun 9, 2012. June 22-23, 2012. We will present a poster of Biips.

5

Probabilistic Programming Reading Group at the University of Oxford · Biips

http://biips.github.io/event/2014/11/10/oxpprg2014.html

Middot; Bayesian inference with interacting particle systems. Probabilistic Programming Reading Group at the University of Oxford. Nov 10, 2014. October 16, 2014. We presented Biips at Oxford PPRG.

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Biips · Bayesian inference with interacting particle systems

Middot; Bayesian inference with interacting particle systems. Logo by Timothée Del Moral. Is a general software for Bayesian inference with interacting particle systems. Aka sequential Monte Carlo (SMC) methods. It aims at popularizing the use of these methods to non-statistician researchers and students, thanks to its automated black box inference engine. It borrows from the BUGS. BUGS language compiler adapted from JAGS. SMC techniques for filtering and smoothing. Core developped in C. May 27, 2015.

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