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Bayes Rules!
An Introduction to Applied Bayesian Modeling
Taschenbuch von Alicia A. Johnson (u. a.)
Sprache: Englisch

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Beschreibung
This book brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. It empowers readers to weave Bayesian approaches into their everyday practice.
This book brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. It empowers readers to weave Bayesian approaches into their everyday practice.
Über den Autor

Alicia Johnson is an Associate Professor of Statistics at Macalester College in Saint Paul, Minnesota. She enjoys exploring and connecting students to Bayesian analysis, computational statistics, and the power of data in contributing to this shared world of ours.

Miles Ott is a Senior Data Scientist at The Janssen Pharmaceutical Companies of Johnson & Johnson. Prior to his current position, he taught at Carleton College, Augsburg University, and Smith College. He is interested in biostatistics, LGBTQ+ health research, analysis of social network data, and statistics/data science education. He blogs at [...] and tweets about statistics, gardening, and his dogs on Twitter.

Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. She spends majority of her time thinking about what to teach, how to teach it, and what tools to use while teaching. She likes intersectional feminism, cats, and R Ladies. She tweets about statistics and data science education on Twitter.

Inhaltsverzeichnis

1 The Big (Bayesian) Picture 2 Bayes' Rule 3 The Beta-Binomial Bayesian Model 4 Balance and Sequentiality in Bayesian Analyses 5 Conjugate Families 6 Approximating the Posterior 7 MCMC Under the Hood 8 Posterior Inference and Prediction 9 Simple Normal Regression 10 Evaluating Regression Models 11 Extending the Normal Regression Model 12 Poisson and Negative Binomial Regression 13 Logistic Regression 14 Naive Bayes Classification 15 Hierarchical Models are Exciting 16 (Normal) Hierarchical Models Without Predictors 17 (Normal) Hierarchical Models With Predictors 18 Non-Normal Hierarchical Regression & Classification 19 Adding More Layers

Details
Erscheinungsjahr: 2022
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 544
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9780367255398
ISBN-10: 0367255391
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Johnson, Alicia A.
Ott, Miles Q.
Dogucu, Mine
Hersteller: Taylor & Francis Ltd
Maße: 256 x 180 x 32 mm
Von/Mit: Alicia A. Johnson (u. a.)
Erscheinungsdatum: 04.03.2022
Gewicht: 1,127 kg
preigu-id: 120543061
Über den Autor

Alicia Johnson is an Associate Professor of Statistics at Macalester College in Saint Paul, Minnesota. She enjoys exploring and connecting students to Bayesian analysis, computational statistics, and the power of data in contributing to this shared world of ours.

Miles Ott is a Senior Data Scientist at The Janssen Pharmaceutical Companies of Johnson & Johnson. Prior to his current position, he taught at Carleton College, Augsburg University, and Smith College. He is interested in biostatistics, LGBTQ+ health research, analysis of social network data, and statistics/data science education. He blogs at [...] and tweets about statistics, gardening, and his dogs on Twitter.

Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. She spends majority of her time thinking about what to teach, how to teach it, and what tools to use while teaching. She likes intersectional feminism, cats, and R Ladies. She tweets about statistics and data science education on Twitter.

Inhaltsverzeichnis

1 The Big (Bayesian) Picture 2 Bayes' Rule 3 The Beta-Binomial Bayesian Model 4 Balance and Sequentiality in Bayesian Analyses 5 Conjugate Families 6 Approximating the Posterior 7 MCMC Under the Hood 8 Posterior Inference and Prediction 9 Simple Normal Regression 10 Evaluating Regression Models 11 Extending the Normal Regression Model 12 Poisson and Negative Binomial Regression 13 Logistic Regression 14 Naive Bayes Classification 15 Hierarchical Models are Exciting 16 (Normal) Hierarchical Models Without Predictors 17 (Normal) Hierarchical Models With Predictors 18 Non-Normal Hierarchical Regression & Classification 19 Adding More Layers

Details
Erscheinungsjahr: 2022
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 544
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9780367255398
ISBN-10: 0367255391
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Johnson, Alicia A.
Ott, Miles Q.
Dogucu, Mine
Hersteller: Taylor & Francis Ltd
Maße: 256 x 180 x 32 mm
Von/Mit: Alicia A. Johnson (u. a.)
Erscheinungsdatum: 04.03.2022
Gewicht: 1,127 kg
preigu-id: 120543061
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