Umfang:
xxii, 398 Seiten
,
graphische Darstellungen
Ausgabe:
First edition
ISBN:
9780367894368
,
9781032180298
Serie:
Texts in statistical science series
Inhalt:
"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. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries."
Anmerkung:
Includes bibliographical references and index
,
Literaturverzeichnis: Seite 387-395
Weitere Ausg.:
10.1201/9781003019169
Weitere Ausg.:
ISBN 9781003019169
Weitere Ausg.:
Erscheint auch als Online-Ausgabe Martin, Osvaldo A Bayesian modeling and computation in python Boca Raton : CRC Press, 2022
Weitere Ausg.:
Erscheint auch als Online-Ausgabe Martin, Osvaldo Bayesian modeling and computation in Python Boca Raton : CRC Press, Taylor & Francis Group, 2022 ISBN 9781003019169
Weitere Ausg.:
ISBN 1003019161
Weitere Ausg.:
ISBN 9781000520040
Weitere Ausg.:
ISBN 1000520048
Weitere Ausg.:
ISBN 9781000520071
Weitere Ausg.:
ISBN 1000520072
Sprache:
Englisch
Fachgebiete:
Psychologie
Schlagwort(e):
Bayes-Modell
;
Bayes-Verfahren
;
Probabilistische Programmierung
;
Zeitreihenanalyse
;
Approximate Bayesian Computation
;
Python
