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

T.W. van de Laar; M.G.H. Cox; A. de Vries

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

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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 language	English
Title of host publication	JuliaCon
Publication status	Published - 8 Aug 2018
Event	JuliaCon 2018 - University College London, London, United Kingdom Duration: 7 Aug 2018 → 11 Aug 2018 http://juliacon.org/2018/

Conference

Conference	JuliaCon 2018
Country/Territory	United Kingdom
City	London
Period	7/08/18 → 11/08/18
Internet address	http://juliacon.org/2018/

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 = aug,

day = "8",

language = "English",

booktitle = "JuliaCon",

note = "JuliaCon 2018 ; Conference date: 07-08-2018 Through 11-08-2018",

url = "http://juliacon.org/2018/",

}

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

T2 - JuliaCon 2018

Y2 - 7 August 2018 through 11 August 2018

ER -

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

Abstract

Conference

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