Bayesian Modeling and Computation in Python

Osvaldo A. Martin

ISBN 10: 036789436X / ISBN 13: 9780367894368

Edité par Taylor & Francis Ltd, 2021

A propos de cet article

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N° de réf. du vendeur C9780367894368

Détails bibliographiques

• Titre: Bayesian Modeling and Computation in Python
• Éditeur: Taylor & Francis Ltd
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 036789436X
• ISBN à 13 chiffres: 9780367894368
• Reliure: Hardback
• État de l'article: New

A propos de ce titre

Synopsis

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory.

À propos de l?auteur

Osvaldo A. Martin is a Researcher at IMASL-CONICET in Argentina and the Department of Computer Science from Aalto University in Finland. He has a PhD in biophysics and structural bioinformatics. Over the years he has become increasingly interested in data analysis problems with a Bayesian flavor. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling.

Ravin Kumar is a Data Scientist at Google and previously worked at SpaceX and sweetgreen among other companies. He has an M.S in Manufacturing Engineering and a B.S in Mechanical Engineering. He found Bayesian statistics to be an excellent tool for modeling organizations and informing strategy. This interest in flexible statistical modeling led to a warm welcoming open source community which he is honored to be a member of now.

Junpeng Lao is a Data Scientist at Google. Prior to that he did his PhD and subsequently worked as a postdoc in Cognitive Neuroscience. He developed a fondness for Bayesian Statistics and generative modeling after working primarily with Bootstrapping and Permutation during his academic life.