Bayesian statistics for beginners: a step-by-step approach
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford University Press
2019
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | X, 419 Seiten Illustrationen, Diagramme |
| ISBN: | 9780198841296 9780198841302 |
Internformat
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| 505 | 8 | |a Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields. | |
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Datensatz im Suchindex
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|---|---|
| adam_text | ComıtemiÎs SECTION! Basics of Probability 1 Introduction to Probability 2 Joint, Marginal, and Conditional Probability 3 11 SECTION 2 Bayes Theorem and Bayesian Inference 3 Bayes Theorem 29 4 Bayesian Inference 37 5 The Author Problem: Bayesian Inference with Two Hypotheses 48 6 The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses 61 7 The Portrait Problem: Bayesian Inference with Joint Likelihood 73 SECTION 3 Probability Functions 8 Probability Mass Functions 9 Probability Density Functions 87 108 SECTION 4 Bayesian Conjugates 10 The White House Problem: The Beta-Binomial Conjugate 133 11 The Shark Attack Problem: The Gamma-Poisson Conjugate 150 12 The Maple Syrup Problem: The Normal-Normal Conjugate 172 SECTION 5 Markov Chain Monte Carlo 13 The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm 193 14 MCMC Diagnostic Approaches 212 15 The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm 224 16 The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling 247
CONTENTS SECTION 6 Applications 17 The Survivor Problem: Simple Linear Regression with MCMC 269 18 The Survivor Problem Continued: Introduction to Bayesian Model Selection 308 19 The Lorax Problem: Introduction to Bayesian Networks 325 20 The Once-ler Problem: Introduction to Decision Trees 353 Appendices A.l The Beta-Binomial Conjugate Solution 369 A.2 The Gamma-Poisson Conjugate Solution 373 A.3 The Normal-Normal Conjugate Solution 379 A.4 Conjugate Solutions for Simple Linear Regression 385 A.5 The Standardization of Regression Data 395 Bibliography Hyperlinks Name Index Subject Index 399 403 413 414
|
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| author | Donovan, Therese M. 1962- Mickey, Ruth M. 1954- |
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| contents | Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields. |
| ctrlnum | (OCoLC)1104911609 (DE-599)BVBBV045566868 |
| discipline | Politologie Mathematik Wirtschaftswissenschaften |
| edition | First edition |
| format | Book |
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| institution | BVB |
| isbn | 9780198841296 9780198841302 |
| language | English |
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| spelling | Donovan, Therese M. 1962- Verfasser (DE-588)1190678349 aut Bayesian statistics for beginners a step-by-step approach Therese M. Donovan, Ruth M. Mickey First edition Oxford Oxford University Press 2019 X, 419 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields. bicssc / Maths for scientists bicssc / Probability & statistics bicssc / Mathematics Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s DE-604 Mickey, Ruth M. 1954- Verfasser (DE-588)141224371 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030950518&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Donovan, Therese M. 1962- Mickey, Ruth M. 1954- Bayesian statistics for beginners a step-by-step approach Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields. bicssc / Maths for scientists bicssc / Probability & statistics bicssc / Mathematics Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4144220-9 |
| title | Bayesian statistics for beginners a step-by-step approach |
| title_auth | Bayesian statistics for beginners a step-by-step approach |
| title_exact_search | Bayesian statistics for beginners a step-by-step approach |
| title_full | Bayesian statistics for beginners a step-by-step approach Therese M. Donovan, Ruth M. Mickey |
| title_fullStr | Bayesian statistics for beginners a step-by-step approach Therese M. Donovan, Ruth M. Mickey |
| title_full_unstemmed | Bayesian statistics for beginners a step-by-step approach Therese M. Donovan, Ruth M. Mickey |
| title_short | Bayesian statistics for beginners |
| title_sort | bayesian statistics for beginners a step by step approach |
| title_sub | a step-by-step approach |
| topic | bicssc / Maths for scientists bicssc / Probability & statistics bicssc / Mathematics Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | bicssc / Maths for scientists bicssc / Probability & statistics bicssc / Mathematics Bayes-Entscheidungstheorie |
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