Chad Scherrer's guest appearances

Chad Scherrer

Learning Bayesian Statistics

David Spiegelhalter

Andrew Gelman

Michael Lee

Baba Brinkman

Michael Kennedy

Allen Downey

Sean Taylor

Michael Faber

Matthew Kay

Michael Betancourt

Eric Ma

Karin Knudson

Colin Carroll

Osvaldo Martin

Chris Fonnesbeck

OwlTail

How is Julia doing? I’m talking about the programming language, of course! What does the probabilistic programming landscape in Julia look like? What are Julia’s distinctive features, and when would it be interesting to use it?To talk about that, I invited Chad Scherrer. Chad is a Senior Research Scientist at RelationalAI, a company that uses Artificial Intelligence technologies to solve business problems.Coming from a mathematics background, Chad did his PhD at Indiana University of Bloomington and has been working in statistics and data science for a decade now. Through this experience, he’s been using and developing probabilistic programming languages – so he’s familiar with python, R, PyMC, Stan and all the blockbusters of the field. But since 2018, he’s particularly interested in Julia and developed Soss, an open-source lightweight probabilistic programming package for Julia. In this episode, he’ll tell us why he decided to create this package, and which choices he made that made Soss what it is today. But we’ll also talk about other projects in Julia, like Turing or Gen for instance.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Chad's Website: https://cscherrer.github.io/Chad on Twitter: https://twitter.com/ChadScherrerSoss Package: https://github.com/cscherrer/Soss.jlSoss Presentation at 2019 Strata NYC: https://slides.com/cscherrer/2019-09-26-strata#/Passage -- A Parallel Sampler Generator for Hierarchical Bayesian Modeling: https://bit.ly/2UTmaYBDynamic HMC in Julia: https://github.com/tpapp/DynamicHMC.jlAdvanced HMC in Julia: https://github.com/TuringLang/AdvancedHMC.jlMonte Carlo Measurements in Julia: https://github.com/baggepinnen/MonteCarloMeasurements.jlTuring.jl -- Bayesian inference with probabilistic programming: https://turing.ml/dev/Gen.jl -- Probabilistic modeling and inference in Julia: https://www.gen.dev/Etalumis -- Bringing Probabilistic Programming to Scientific Simulators at Scale: https://arxiv.org/abs/1907.03382Omega.jl -- A programming language for causal and probabilistic reasoning: http://www.zenna.org/Omega.jl/latest/JuliaLang -- The Ingredients for a Composable Programming Language: https://white.ucc.asn.au/2020/02/09/whycompositionaljulia.htmlSimpy -- Discrete event simulation for Python: https://simpy.readthedocs.io/en/latest/This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

How is Julia doing? I’m talking about the programming language, of course! What does the probabilistic programming landscape in Julia look like? What are Julia’s distinctive features, and when would it be interesting to use it?To talk about that, I invited Chad Scherrer. Chad is a Senior Research Scientist at RelationalAI, a company that uses Artificial Intelligence technologies to solve business problems.Coming from a mathematics background, Chad did his PhD at Indiana University of Bloomington and has been working in statistics and data science for a decade now. Through this experience, he’s been using and developing probabilistic programming languages – so he’s familiar with python, R, PyMC, Stan and all the blockbusters of the field. But since 2018, he’s particularly interested in Julia and developed Soss, an open-source lightweight probabilistic programming package for Julia. In this episode, he’ll tell us why he decided to create this package, and which choices he made that made Soss what it is today. But we’ll also talk about other projects in Julia, like Turing or Gen for instance.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !Links from the show:<ul><li>Chad's Website: <a href="https://cscherrer.github.io/" rel="noopener noreferrer nofollow" target="_blank">https://cscherrer.github.io/</a></li><li>Chad on Twitter: <a href="https://twitter.com/ChadScherrer" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/ChadScherrer</a></li><li>Soss Package: <a href="https://github.com/cscherrer/Soss.jl" rel="noopener noreferrer nofollow" target="_blank">https://github.com/cscherrer/Soss.jl</a></li><li>Soss Presentation at 2019 Strata NYC: <a href="https://slides.com/cscherrer/2019-09-26-strata#/" rel="noopener noreferrer nofollow" target="_blank">https://slides.com/cscherrer/2019-09-26-strata#/</a></li><li>Passage -- A Parallel Sampler Generator for Hierarchical Bayesian Modeling: <a href="https://bit.ly/2UTmaYB" rel="noopener noreferrer nofollow" target="_blank">https://bit.ly/2UTmaYB</a></li><li>Dynamic HMC in Julia: <a href="https://github.com/tpapp/DynamicHMC.jl" rel="noopener noreferrer nofollow" target="_blank">https://github.com/tpapp/DynamicHMC.jl</a></li><li>Advanced HMC in Julia: <a href="https://github.com/TuringLang/AdvancedHMC.jl" rel="noopener noreferrer nofollow" target="_blank">https://github.com/TuringLang/AdvancedHMC.jl</a></li><li>Monte Carlo Measurements in Julia: <a href="https://github.com/baggepinnen/MonteCarloMeasurements.jl" rel="noopener noreferrer nofollow" target="_blank">https://github.com/baggepinnen/MonteCarloMeasurements.jl</a></li><li>Turing.jl -- Bayesian inference with probabilistic programming: <a href="https://turing.ml/dev/" rel="noopener noreferrer nofollow" target="_blank">https://turing.ml/dev/</a></li><li>Gen.jl -- Probabilistic modeling and inference in Julia: <a href="https://www.gen.dev/" rel="noopener noreferrer nofollow" target="_blank">https://www.gen.dev/</a></li><li>Etalumis -- Bringing Probabilistic Programming to Scientific Simulators at Scale: <a href="https://arxiv.org/abs/1907.03382" rel="noopener noreferrer nofollow" target="_blank">https://arxiv.org/abs/1907.03382</a></li><li>Omega.jl -- A programming language for causal and probabilistic reasoning: <a href="http://www.zenna.org/Omega.jl/latest/" rel="noopener noreferrer nofollow" target="_blank">http://www.zenna.org/Omega.jl/latest/</a></li><li>JuliaLang -- The Ingredients for a Composable Programming Language: <a href="https://white.ucc.asn.au/2020/02/09/whycompositionaljulia.html" rel="noopener noreferrer nofollow" target="_blank">https://white.ucc.asn.au/2020/02/09/whycompositionaljulia.html</a></li><li>Simpy -- Discrete event simulation for Python: <a href="https://simpy.readthedocs.io/en/latest/" rel="noopener noreferrer nofollow" target="_blank">https://simpy.readthedocs.io/en/latest/</a></li></ul>This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

#13 Building a Probabilistic Programming Framework in Julia, with Chad Scherrer