Biips is a general software for Bayesian inference with interacting particle systems, a.k.a. 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/JAGS software, widely used in Bayesian statistics, the statistical modeling with graphical models and the language associated with their descriptions.


Bayesian inference consists in approximating an unknown parameter dependent conditional probability law given a set of observations. A large number of problems, e.g. non-supervised classification, filtering, etc., can be addressed having as basis the aforementioned formulation. The underlying probability law, while not calculable in analytical manner for the general case, can be approximated by using Monte Carlo Markov Chain (MCMC) methods. These methods are popular for bayesian inference thanks to BUGS software and the WinBUGS graphical interface.

Emerged as a result of recent research studies, interacting particle based algorithms – a.k.a. Sequential Monte Carlo (SMC) methods in which the most common implementation is the particle filter – proved to have superior performances when compared to classical MCMC approaches. What is more, interacting particle algorithms are well adapted for dynamic estimation problems as encountered, for example, in filtering, tracking or classification problems. They do not require burn in convergence time and include calculation of the normalizing constant.


  • BUGS language compiler adapted from JAGS
  • Black-box inference engine with:
    • SMC/particle algorithms for filtering and smoothing
    • Static parameter estimation using particle MCMC
    • Automatic choice of the proposal samplers
  • Core library in C++
  • R interface: Rbiips
  • Matlab/Octave interface: Matbiips
  • Easy language extensions with custom R and Matlab functions
  • Multi-platform: Linux, Windows, OSX
  • Free and open source (GPL)


  • Parallelization
  • Particle Gibbs algorithm


To contact us with non-public matters, please write to Otherwise you can use the discusssion forum.

Getting Help

The discussion forum can be used for any question or feedback about Biips. This is the best place for getting help. All Biips users are invited to participate.

We recommend creating an account at GForge Inria. In the registration form, you can enter “ALEA” in the INRIA research team field.


The core software libraries are written in C++. An R package allows running Biips from the R statistical software and provides posterior analysis and plotting functions. A Matlab toolbox provides similar capabilities.

The Biips source code is hosted on a git repository at GForge Inria. If you want to keep track of the development version, you can find downloading instructions on the GForge Biips project.


Biips is licensed under the GPL-3 License. You may freely modify and redistribute it under certain conditions (see the file COPYING for details).

The source code is hosted at GForge Inria.


Biips code is Copyright (C) Inria, 2010-2014.


Biips code is adapted from:

  • JAGS, Copyright (C) Martyn Plummer, 2002-2010
  • SMCTC, Copyright (C) Adam M. Johansen, 2008-2009

Additional information concerning adapted open source software is included in the file NOTICES.