Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1712
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dc.contributor.authorMartin, Osvaldo-
dc.date.accessioned2026-07-31T09:57:37Z-
dc.date.available2026-07-31T09:57:37Z-
dc.date.issued2018-12-
dc.identifier.isbn978-1-78934-165-2-
dc.identifier.urihttp://hdl.handle.net/123456789/1712-
dc.description.abstractBayesian statistics has been developing for more than 250 years. During this time, it has enjoyed as much recognition and appreciation as it has faced disdain and contempt. Throughout the last few decades, it has gained more and more attention from people in statistics and almost all the other sciences, engineering, and even outside the boundaries of the academic world. This revival has been possible due to theoretical and computational advancements developed mostly throughout the second half of the 20th century. Indeed, modern Bayesian statistics is mostly computational statistics. The necessity for flexible and transparent models and a more intuitive interpretation of statistical models and analysis has only contributed to the trend.en_US
dc.language.isoenen_US
dc.relation.ispartofseries1241218;-
dc.subjectBayesian Analysis with Pythonen_US
dc.titleBayesian Analysis with Pythonen_US
dc.title.alternativeIntroduction to statistical modeling and probabilistic programming using PyMC3 and ArviZen_US
dc.typeBooken_US
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