Approximate Bayesian computation: Difference between revisions

Content deleted Content added
add pymc as a software for ABC
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<ref name="Kangas16">{{cite journal |last1= Kangasrääsiö |first1= Antti |last2= Lintusaari |first2= Jarno |last3= Skytén |first3= Kusti |last4= Järvenpää |first4= Marko |last5= Vuollekoski |first5= Henri |last6= Gutmann |first6= Michael |last7= Vehtari |first7= Aki |last8= Corander |first8= Jukka |last9= Kaski |first9= Samuel|year= 2016 |title= ELFI: Engine for Likelihood-Free Inference |url=http://approximateinference.org/accepted/KangasraasioEtAl2016.pdf |journal= NIPS 2016 Workshop on Advances in Approximate Bayesian Inference|bibcode= 2017arXiv170800707L |arxiv= 1708.00707 }}</ref>
<ref name="Klinger2017">Klinger, E.; Rickert, D.; Hasenauer, J. (2017). pyABC: distributed, likelihood-free inference.</ref>
<ref name="Salvatier2016">Salvatier J, Wiecki TV, Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 https://doi.org/10.7717/peerj-cs.55</ref>
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