Osvaldo A. Martin, Ravin Kumar, Junpeng Lao

Bayesian Modeling and Computation in Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 420
• Erscheinungstermin: 28. Dezember 2021
• Englisch
• ISBN-13: 9781000520040
• Artikelnr.: 63068733

Autorenporträt

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.

Inhaltsangabe

1. Bayesian Inference 2. Exploratory Analysis of Bayesian Models 3. Linear
Models and Probabilistic Programming Languages 4. Extending Linear Models
5. Splines 6. Time Series 7. Bayesian Additive Regression Trees 8.
Approximate Bayesian Computation 9. End to End Bayesian Workflows 10.
Probabilistic Programming Languages 11. Appendiceal Topics