ForneyLab.jl: a Julia toolbox for factor graph-based probabilistic programming

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Abstract

Scientific modeling concerns a continual search for better models for given data sets. This process can be elegantly captured in a Bayesian inference framework. ForneyLab enables largely automated scientific design loops by deriving fast, analytic algorithms for approximate Bayesian inference.
Original languageEnglish
Title of host publicationJuliaCon
Publication statusPublished - 8 Aug 2018
EventJuliaCon 2018 - University College London, London, United Kingdom
Duration: 7 Aug 201811 Aug 2018
http://juliacon.org/2018/

Conference

ConferenceJuliaCon 2018
CountryUnited Kingdom
CityLondon
Period7/08/1811/08/18
Internet address

Cite this

@inproceedings{6af604f5caa34bf6af33640444ca00ac,
title = "ForneyLab.jl: a Julia toolbox for factor graph-based probabilistic programming",
abstract = "Scientific modeling concerns a continual search for better models for given data sets. This process can be elegantly captured in a Bayesian inference framework. ForneyLab enables largely automated scientific design loops by deriving fast, analytic algorithms for approximate Bayesian inference.",
author = "{van de Laar}, T.W. and M.G.H. Cox and {de Vries}, A.",
year = "2018",
month = "8",
day = "8",
language = "English",
booktitle = "JuliaCon",

}

ForneyLab.jl: a Julia toolbox for factor graph-based probabilistic programming. / van de Laar, T.W.; Cox, M.G.H.; de Vries, A.

JuliaCon. 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

TY - GEN

T1 - ForneyLab.jl: a Julia toolbox for factor graph-based probabilistic programming

AU - van de Laar, T.W.

AU - Cox, M.G.H.

AU - de Vries, A.

PY - 2018/8/8

Y1 - 2018/8/8

N2 - Scientific modeling concerns a continual search for better models for given data sets. This process can be elegantly captured in a Bayesian inference framework. ForneyLab enables largely automated scientific design loops by deriving fast, analytic algorithms for approximate Bayesian inference.

AB - Scientific modeling concerns a continual search for better models for given data sets. This process can be elegantly captured in a Bayesian inference framework. ForneyLab enables largely automated scientific design loops by deriving fast, analytic algorithms for approximate Bayesian inference.

UR - https://youtu.be/RS4hJ4oBr9c

M3 - Conference contribution

BT - JuliaCon

ER -