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Line 22: More recently, the probabilistic programming system [[Turing (probabilistic programming)\|Turing.jl]] has been applied in various pharmaceutical<ref>{{Cite journal\|last1=Semenova\|first1=Elizaveta\|last2=Williams\|first2=Dominic P.\|last3=Afzal\|first3=Avid M.\|last4=Lazic\|first4=Stanley E.\|date=2020-11-01\|title=A Bayesian neural network for toxicity prediction\|url=https://www.sciencedirect.com/science/article/pii/S2468111320300438\|journal=Computational Toxicology\|language=en\|volume=16\|pages=100133\|doi=10.1016/j.comtox.2020.100133\|s2cid=225362130\|issn=2468-1113}}</ref> and economics applications.<ref name="pharma-turing">{{Citation\|title= Predicting Drug-Induced Liver Injury with Bayesian Machine Learning \|year= 2020 \|doi= 10.1021/acs.chemrestox.9b00264 \|url= https://pubs.acs.org/doi/10.1021/acs.chemrestox.9b00264\|last1= Williams \|first1= Dominic P. \|last2= Lazic \|first2= Stanley E. \|last3= Foster \|first3= Alison J. \|last4= Semenova \|first4= Elizaveta \|last5= Morgan \|first5= Paul \|journal= Chemical Research in Toxicology \|volume= 33 \|issue= 1 \|pages= 239–248 \|pmid= 31535850 \|s2cid= 202689667 }}</ref> Probabilistic programming in Julia has also been combined with [[differentiable programming]] by combining the Julia package Zygote.jl with Turing.jl. <ref name="diffprog-zygote">{{cite ~~arxiv~~arXiv\|date=2019\|title=∂P: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing\|~~arxiv~~eprint=1907.07587\|last1=Innes\|first1=Mike\|last2=Edelman\|first2=Alan\|last3=Fischer\|first3=Keno\|last4=Rackauckas\|first4=Chris\|last5=Saba\|first5=Elliot\|author6=Viral B Shah\|last7=Tebbutt\|first7=Will\|class=cs.PL }}</ref> Probabilistic programming languages are also commonly used in [[Bayesian cognitive science]] to develop and evaluate models of cognition. <ref>{{cite web \|last1=Goodman \|first1=Noah D \|last2=Tenenbaum \|first2=Joshua B \|last3=Buchsbaum \|first3=Daphna \|last4=Hartshorne \|first4=Joshua \|last5=Hawkins \|first5=Robert \|last6=O'Donnell \|first6=Timothy J \|last7=Tessler \|first7=Michael Henry \|title=Probabilistic Models of Cognition \|url=http://probmods.org/ \|website=Probabilistic Models of Cognition - 2nd Edition \|access-date=27 May 2023}}</ref> Line 190: \| \|- \|MultiVerse<ref>{{Citation \|last1=Perov \|first1=Yura \|title=MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming \|date=2020-01-28 ~~\|url=http://arxiv.org/abs/1910.08091 \|access-date=2024-01-06~~ \|arxiv=1910.08091 \|last2=Graham \|first2=Logan \|last3=Gourgoulias \|first3=Kostis \|last4=Richens \|first4=Jonathan G. \|last5=Lee \|first5=Ciarán M. \|last6=Baker \|first6=Adam \|last7=Johri \|first7=Saurabh}}</ref> \|Python \|Python

Probabilistic programming: Difference between revisions