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Bayesian Analysis with Python - Second Edition

Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ
BuchKartoniert, Paperback
356 Seiten
Englisch
Packt Publishingerschienen am26.12.2018Second
Bayesian inference uses probability distributions and Bayes' theorem to build flexible models. The book uses PyMC3 to abstract all the mathematical and computational details from this process allowing readers to solve a wide range of problems in data science.mehr

Produkt

KlappentextBayesian inference uses probability distributions and Bayes' theorem to build flexible models. The book uses PyMC3 to abstract all the mathematical and computational details from this process allowing readers to solve a wide range of problems in data science.
Details
ISBN/GTIN978-1-78934-165-2
ProduktartBuch
EinbandartKartoniert, Paperback
Erscheinungsjahr2018
Erscheinungsdatum26.12.2018
AuflageSecond
Seiten356 Seiten
SpracheEnglisch
MasseBreite 191 mm, Höhe 235 mm, Dicke 20 mm
Gewicht665 g
Artikel-Nr.49149155

Inhalt/Kritik

Inhaltsverzeichnis
Table of ContentsThinking probabilisticallyProgramming probabilisticallyModeling with Linear RegressionGeneralizing Linear ModelsModel ComparisonMixture ModelsGaussian ProcessesInference EnginesWhere To Go Next?mehr

Autor

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.