Biips: Software for Bayesian Inference with Interacting Particle Systems
By Adrien Todeschini, F. Caron, Marc Fuentes, Pierrick Legrand, Pierre Del Moral. 2014
26 citations recorded by the source corpus.
Biips is a software platform for automatic Bayesian inference with interacting particle systems. Biips allows users to define their statistical model in the probabilistic programming BUGS language, as well as to add custom functions or samplers within this language. Then it runs sequential Monte Carlo based algorithms (particle filters, particle independent Metropolis-Hastings, particle marginal Metropolis-Hastings) in a black-box manner so that to approximate the posterior distribution of interest as well as the marginal likelihood. The software is developed in C++ with interfaces with the softwares R, Matlab and Octave.
Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yura N. Perov
- Brooks Paige
- Frank D. Wood
- Thomas Bo Schön
- Daniel Lundén
- Jan Kudlicka
- David Broman
- Lawrence M. Murray
In Probabilistic & Bayesian Methods
- Yura N. Perov
- Frank D. Wood
- Brooks Paige
- Thomas Bo Schön
- Daniel Lundén
- Jan Kudlicka
- David Broman
- Lawrence M. Murray
Citations in the corpus
- An Introduction to Probabilistic Programming (2018, neutral): “, 1997), (Thrun, 2000); and statistics: Biips (Todeschini et al., 2014), LibBi (Murray, 2013), Birch (Murray et al.”
- Inference Networks for Sequential Monte Carlo in Graphical Models (2016, background): “Particularly relevant recent work directed towards improving SMC inference in the same class of models we address includes the Biips ordering and arrangement algorithm (Todeschini et al., 2014), the divide and conquer approach (Lindsten et al.”
- Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs (2017, methodology): “…Carlo (SMC) inference algorithms [4], extending an arc of research that includes probabilistic programming languages (PPLs) such as Venture [14], Anglican [26], Probabilistic C [18], WebPPL [8], Fi-garo [20], and Turing [7], as well as similarly-motivated software such as LibBi [16] and BiiPS [25].”
- Applications of Probabilistic Programming (Master's thesis, 2015) (2016, methodology): “For example, two new probabilistic programming languages have recently been introduced, employing sequential Monte Carlo methods (Smith et al., 2013): Anglican (Wood et al., 2014) and Biips (Todeschini et al., 2014).”