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Calculated Shard: 45 (from laksa191)

2. Crawled Status Check

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{"found_url":"https:\/\/open.umn.edu\/opentextbooks\/textbooks\/think-bayes-bayesian-statistics-made-simple","crawl_time":1774099477,"first_indexed_time":0,"http_code":200,"src_unparsed":"edu,umn!open,\/opentextbooks\/textbooks\/think-bayes-bayesian-statistics-made-simple s443","src_root_hash":"1624480930799954845","history_drop_reason":null,"meta_title":"Think Bayes: Bayesian Statistics Made Simple - Open Textbook Library","meta_descriptions":["Think Bayes is an introduction to Bayesian statistics using computational methods."],"attrs_boilerpipe_text":"(1 review)\nAllen B. Downey, Franklin W. Olin College of Engineering\nCopyright Year:\n2012\nISBN 13:\n9781449370787\nPublisher:\nGreen Tea Press\nLanguage:\nEnglish\nFormats Available\nOnline\nPDF\nLaTeX\nHardcopy\nConditions of Use\nAttribution-NonCommercial\nCC BY-NC\nReviews\nLearn more about reviews.\nReviewed by Edwin Chong, Professor, Colorado State University on 12\/5\/16\nThe book is appropriately comprehensive, covering the basics as well\nas interesting and important applications of Bayesian methods.\nread more\nTable of Contents\nPreface\n1 Bayes's Theorem\n2 Computational Statistics\n3 Estimation\n4 More Estimation\n5 Odds and Addends\n6 Decision Analysis\n7 Prediction\n8 Observer Bias\n9 Two Dimensions\n10 Approximate Bayesian Computation\n11 Hypothesis Testing\n12 Evidence\n13 Simulation\n14 A Hierarchical Model\n15 Dealing with Dimensions\nAbout the Book\nThink Bayes\nis an introduction to Bayesian statistics using computational methods.\nThe premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.\nMost books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.\nI think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.\nAuthor\nAllen B. Downey\nis an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc.\nAncillaries\nHomework\nGreen Tea Press\nSubmit ancillary resource\nContribute to this Page\nSuggest an edit to this book record","attrs_markdown":"[Skip to content](https:\/\/open.umn.edu\/opentextbooks\/textbooks\/think-bayes-bayesian-statistics-made-simple#main)\n\n[![Library Home](https:\/\/open.umn.edu\/assets\/library\/otl_logo-8abb93f0.webp)](https:\/\/open.umn.edu\/opentextbooks\/)\n\n- [Browse Subjects](https:\/\/open.umn.edu\/opentextbooks\/subjects)\n- [About Open Textbooks](https:\/\/open.umn.edu\/opentextbooks\/books)\n  - [FAQ](https:\/\/open.umn.edu\/opentextbooks\/faq)\n  - [Friends](https:\/\/open.umn.edu\/opentextbooks\/friends)\n- [Submit](https:\/\/open.umn.edu\/opentextbooks\/textbooks\/submit)\n- [Discovery](https:\/\/open.umn.edu\/opentextbooks\/discovery)\n- [Open Education Network](https:\/\/open.umn.edu\/oen)\n\nYou are offline and only viewing locally cached content.\n\n![Read more about Think Bayes: Bayesian Statistics Made Simple](https:\/\/open.umn.edu\/rails\/active_storage\/representations\/redirect\/eyJfcmFpbHMiOnsiZGF0YSI6MjI5LCJwdXIiOiJibG9iX2lkIn19--e348f188f8d45caa76d6e62fe9baa49df41d7704\/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJ3ZWJwIiwicmVzaXplIjoiNDkweDEwMDAifSwicHVyIjoidmFyaWF0aW9uIn19--421ab82856ce4ced5bdaa37f96bcb9060315a4c7\/9781449370787.png?disposition=inline)\n\n# Think Bayes: Bayesian Statistics Made Simple\n[(1 review)](https:\/\/open.umn.edu\/opentextbooks\/textbooks\/think-bayes-bayesian-statistics-made-simple#Reviews)\n\nAllen B. Downey, Franklin W. Olin College of Engineering\n\nCopyright Year: 2012\n\nISBN 13: 9781449370787\n\nPublisher: [Green Tea Press](http:\/\/greenteapress.com\/wp\/think-bayes\/)\n\nLanguage: English\n\n## Formats Available\n- [Online](https:\/\/open.umn.edu\/opentextbooks\/formats\/337)\n- [PDF](https:\/\/open.umn.edu\/opentextbooks\/formats\/338)\n- [LaTeX](https:\/\/open.umn.edu\/opentextbooks\/formats\/1966)\n- [Hardcopy](https:\/\/open.umn.edu\/opentextbooks\/formats\/1967)\n\n## Conditions of Use\n![Attribution-NonCommercial](https:\/\/open.umn.edu\/assets\/library\/licenses\/AttributionNonCommercial-b63ddd6b.webp) [Attribution-NonCommercial](http:\/\/creativecommons.org\/licenses\/)   \n CC BY-NC\n\n## Reviews\n[Learn more about reviews.](https:\/\/open.umn.edu\/opentextbooks\/reviews\/rubric)\n\nReviewed by Edwin Chong, Professor, Colorado State University on 12\/5\/16\n\nThe book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. [read more]()\n\nReviewed by Edwin Chong, Professor, Colorado State University on 12\/5\/16\n\nComprehensiveness  rating: 5 [see less]()\n\nThe book is appropriately comprehensive, covering the basics as well   \nas interesting and important applications of Bayesian methods.\n\nContent Accuracy  rating: 4\n\nGenerally, the book's coverage is accurate. Because the style of the   \nbook is somewhat informal, sometimes there is some lack of precision   \n(but nothing serious).\n\nRelevance\/Longevity  rating: 4\n\nThe approach is currently very relevant. It uses Python code   \nthroughout. I expect Python to continue to be of interest in the   \nnear and probably medium-term future, so the longevity of the book   \nshould be quite good.\n\nClarity  rating: 5\n\nThe writing style is somewhat informal, and so clarity will be high   \nfor newbies. For those wanting a more formal treatment, it would be   \nbetter to look for a more advanced, mathematical treatment.\n\nConsistency  rating: 5\n\nThe entire book is written by a single author, using consistent   \nstyle, terminology, and approach throughout.\n\nModularity  rating: 5\n\nAfter getting the basics down, it should be easy for the reader to   \nread individual chapters and get the key ideas without needing to   \nrefer much to other chapters.\n\nOrganization\/Structure\/Flow  rating: 5\n\nThe organization and flow is excellent. I found it very easy to read   \nthe book cover to cover.\n\nInterface  rating: 5\n\nThe interface is pleasant and easy to navigate. The Python code   \nscattered throughout the book is also easy to identify and follow.\n\nGrammatical Errors  rating: 5\n\nI did not find any significant grammatical issues.\n\nCultural Relevance  rating: 5\n\nThere are no particular cultural issues that I could see.\n\n## Table of Contents\n- Preface\n- 1 Bayes's Theorem\n- 2 Computational Statistics\n- 3 Estimation\n- 4 More Estimation\n- 5 Odds and Addends\n- 6 Decision Analysis\n- 7 Prediction\n- 8 Observer Bias\n- 9 Two Dimensions\n- 10 Approximate Bayesian Computation\n- 11 Hypothesis Testing\n- 12 Evidence\n- 13 Simulation\n- 14 A Hierarchical Model\n- 15 Dealing with Dimensions\n\n## About the Book\n*Think Bayes* is an introduction to Bayesian statistics using computational methods.\n\nThe premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.\n\nMost books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.\n\nI think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.\n\n## About the Contributors\n### Author\n**Allen B. Downey** is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc.\n\n## Ancillaries\n### Homework\n- [Green Tea Press](https:\/\/open.umn.edu\/opentextbooks\/166\/ancillaries\/think-bayes-bayesian-statistics-made-simple)\n\n[Submit ancillary resource](https:\/\/open.umn.edu\/opentextbooks\/ancillaries\/submit?textbook_id=288)\n\n## Contribute to this Page\n[Suggest an edit to this book record](https:\/\/open.umn.edu\/opentextbooks\/textbooks\/think-bayes-bayesian-statistics-made-simple\/edit)\n\nStay Updated\n\n[Bluesky](https:\/\/bsky.app\/profile\/openednetwork.bsky.social)\n\n[Mastodon](https:\/\/mastodon.social\/@OpenEd_Network)\n\n[LinkedIn](https:\/\/www.linkedin.com\/company\/open-education-network)\n\n[YouTube](https:\/\/www.youtube.com\/channel\/UC_lf4_Wss_uW0KGny_A3erg)\n\nJoin Our Newsletter\n\nCENTER FOR OPEN EDUCATION \\| The Open Education Network is based in the Center for Open Education in the University of Minnesota’s College of Education and Human Development.   \n University of Minnesota, 330 Wulling Hall, 86 Pleasant Street S.E., Minneapolis, MN 55455\n\n[![Attribution License](https:\/\/open.umn.edu\/assets\/library\/licenses\/Attribution-4946116f.webp)](http:\/\/creativecommons.org\/licenses\/by\/4.0\/) Except where otherwise noted, content on this site is licensed under a [Creative Commons Attribution 4.0 License](http:\/\/creativecommons.org\/licenses\/by\/4.0\/)","attrs_readable_markdown":"![Read more about Think Bayes: Bayesian Statistics Made Simple](https:\/\/open.umn.edu\/rails\/active_storage\/representations\/redirect\/eyJfcmFpbHMiOnsiZGF0YSI6MjI5LCJwdXIiOiJibG9iX2lkIn19--e348f188f8d45caa76d6e62fe9baa49df41d7704\/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJ3ZWJwIiwicmVzaXplIjoiNDkweDEwMDAifSwicHVyIjoidmFyaWF0aW9uIn19--421ab82856ce4ced5bdaa37f96bcb9060315a4c7\/9781449370787.png?disposition=inline)\n\n[(1 review)](https:\/\/open.umn.edu\/opentextbooks\/textbooks\/think-bayes-bayesian-statistics-made-simple#Reviews)\n\nAllen B. Downey, Franklin W. Olin College of Engineering\n\nCopyright Year: 2012\n\nISBN 13: 9781449370787\n\nPublisher: [Green Tea Press](http:\/\/greenteapress.com\/wp\/think-bayes\/)\n\nLanguage: English\n\n## Formats Available\n- [Online](https:\/\/open.umn.edu\/opentextbooks\/formats\/337)\n- [PDF](https:\/\/open.umn.edu\/opentextbooks\/formats\/338)\n- [LaTeX](https:\/\/open.umn.edu\/opentextbooks\/formats\/1966)\n- [Hardcopy](https:\/\/open.umn.edu\/opentextbooks\/formats\/1967)\n\n## Conditions of Use\n![Attribution-NonCommercial](https:\/\/open.umn.edu\/assets\/library\/licenses\/AttributionNonCommercial-b63ddd6b.webp) [Attribution-NonCommercial](http:\/\/creativecommons.org\/licenses\/)   \n CC BY-NC\n\n## Reviews\n[Learn more about reviews.](https:\/\/open.umn.edu\/opentextbooks\/reviews\/rubric)\n\nReviewed by Edwin Chong, Professor, Colorado State University on 12\/5\/16\n\nThe book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. [read more]()\n\n## Table of Contents\n- Preface\n- 1 Bayes's Theorem\n- 2 Computational Statistics\n- 3 Estimation\n- 4 More Estimation\n- 5 Odds and Addends\n- 6 Decision Analysis\n- 7 Prediction\n- 8 Observer Bias\n- 9 Two Dimensions\n- 10 Approximate Bayesian Computation\n- 11 Hypothesis Testing\n- 12 Evidence\n- 13 Simulation\n- 14 A Hierarchical Model\n- 15 Dealing with Dimensions\n\n## About the Book\n*Think Bayes* is an introduction to Bayesian statistics using computational methods.\n\nThe premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.\n\nMost books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.\n\nI think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.\n\n### Author\n**Allen B. Downey** is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. 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Think Bayes: Bayesian Statistics Made Simple - Open Textbook Library

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Think Bayes is an introduction to Bayesian statistics using computational methods.

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(1 review) Allen B. Downey, Franklin W. Olin College of Engineering Copyright Year: 2012 ISBN 13: 9781449370787 Publisher: Green Tea Press Language: English Formats Available Online PDF LaTeX Hardcopy Conditions of Use Attribution-NonCommercial CC BY-NC Reviews Learn more about reviews. Reviewed by Edwin Chong, Professor, Colorado State University on 12/5/16 The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. read more Table of Contents Preface 1 Bayes's Theorem 2 Computational Statistics 3 Estimation 4 More Estimation 5 Odds and Addends 6 Decision Analysis 7 Prediction 8 Observer Bias 9 Two Dimensions 10 Approximate Bayesian Computation 11 Hypothesis Testing 12 Evidence 13 Simulation 14 A Hierarchical Model 15 Dealing with Dimensions About the Book Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems. Author Allen B. Downey is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc. Ancillaries Homework Green Tea Press Submit ancillary resource Contribute to this Page Suggest an edit to this book record

Markdown

[Skip to content](https://open.umn.edu/opentextbooks/textbooks/think-bayes-bayesian-statistics-made-simple#main) [![Library Home](https://open.umn.edu/assets/library/otl_logo-8abb93f0.webp)](https://open.umn.edu/opentextbooks/) - [Browse Subjects](https://open.umn.edu/opentextbooks/subjects) - [About Open Textbooks](https://open.umn.edu/opentextbooks/books) - [FAQ](https://open.umn.edu/opentextbooks/faq) - [Friends](https://open.umn.edu/opentextbooks/friends) - [Submit](https://open.umn.edu/opentextbooks/textbooks/submit) - [Discovery](https://open.umn.edu/opentextbooks/discovery) - [Open Education Network](https://open.umn.edu/oen) You are offline and only viewing locally cached content. ![Read more about Think Bayes: Bayesian Statistics Made Simple](https://open.umn.edu/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MjI5LCJwdXIiOiJibG9iX2lkIn19--e348f188f8d45caa76d6e62fe9baa49df41d7704/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJ3ZWJwIiwicmVzaXplIjoiNDkweDEwMDAifSwicHVyIjoidmFyaWF0aW9uIn19--421ab82856ce4ced5bdaa37f96bcb9060315a4c7/9781449370787.png?disposition=inline) # Think Bayes: Bayesian Statistics Made Simple [(1 review)](https://open.umn.edu/opentextbooks/textbooks/think-bayes-bayesian-statistics-made-simple#Reviews) Allen B. Downey, Franklin W. Olin College of Engineering Copyright Year: 2012 ISBN 13: 9781449370787 Publisher: [Green Tea Press](http://greenteapress.com/wp/think-bayes/) Language: English ## Formats Available - [Online](https://open.umn.edu/opentextbooks/formats/337) - [PDF](https://open.umn.edu/opentextbooks/formats/338) - [LaTeX](https://open.umn.edu/opentextbooks/formats/1966) - [Hardcopy](https://open.umn.edu/opentextbooks/formats/1967) ## Conditions of Use ![Attribution-NonCommercial](https://open.umn.edu/assets/library/licenses/AttributionNonCommercial-b63ddd6b.webp) [Attribution-NonCommercial](http://creativecommons.org/licenses/) CC BY-NC ## Reviews [Learn more about reviews.](https://open.umn.edu/opentextbooks/reviews/rubric) Reviewed by Edwin Chong, Professor, Colorado State University on 12/5/16 The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. [read more]() Reviewed by Edwin Chong, Professor, Colorado State University on 12/5/16 Comprehensiveness rating: 5 [see less]() The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. Content Accuracy rating: 4 Generally, the book's coverage is accurate. Because the style of the book is somewhat informal, sometimes there is some lack of precision (but nothing serious). Relevance/Longevity rating: 4 The approach is currently very relevant. It uses Python code throughout. I expect Python to continue to be of interest in the near and probably medium-term future, so the longevity of the book should be quite good. Clarity rating: 5 The writing style is somewhat informal, and so clarity will be high for newbies. For those wanting a more formal treatment, it would be better to look for a more advanced, mathematical treatment. Consistency rating: 5 The entire book is written by a single author, using consistent style, terminology, and approach throughout. Modularity rating: 5 After getting the basics down, it should be easy for the reader to read individual chapters and get the key ideas without needing to refer much to other chapters. Organization/Structure/Flow rating: 5 The organization and flow is excellent. I found it very easy to read the book cover to cover. Interface rating: 5 The interface is pleasant and easy to navigate. The Python code scattered throughout the book is also easy to identify and follow. Grammatical Errors rating: 5 I did not find any significant grammatical issues. Cultural Relevance rating: 5 There are no particular cultural issues that I could see. ## Table of Contents - Preface - 1 Bayes's Theorem - 2 Computational Statistics - 3 Estimation - 4 More Estimation - 5 Odds and Addends - 6 Decision Analysis - 7 Prediction - 8 Observer Bias - 9 Two Dimensions - 10 Approximate Bayesian Computation - 11 Hypothesis Testing - 12 Evidence - 13 Simulation - 14 A Hierarchical Model - 15 Dealing with Dimensions ## About the Book *Think Bayes* is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems. ## About the Contributors ### Author **Allen B. Downey** is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc. ## Ancillaries ### Homework - [Green Tea Press](https://open.umn.edu/opentextbooks/166/ancillaries/think-bayes-bayesian-statistics-made-simple) [Submit ancillary resource](https://open.umn.edu/opentextbooks/ancillaries/submit?textbook_id=288) ## Contribute to this Page [Suggest an edit to this book record](https://open.umn.edu/opentextbooks/textbooks/think-bayes-bayesian-statistics-made-simple/edit) Stay Updated [Bluesky](https://bsky.app/profile/openednetwork.bsky.social) [Mastodon](https://mastodon.social/@OpenEd_Network) [LinkedIn](https://www.linkedin.com/company/open-education-network) [YouTube](https://www.youtube.com/channel/UC_lf4_Wss_uW0KGny_A3erg) Join Our Newsletter CENTER FOR OPEN EDUCATION \| The Open Education Network is based in the Center for Open Education in the University of Minnesota’s College of Education and Human Development. 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![Read more about Think Bayes: Bayesian Statistics Made Simple](https://open.umn.edu/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MjI5LCJwdXIiOiJibG9iX2lkIn19--e348f188f8d45caa76d6e62fe9baa49df41d7704/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJ3ZWJwIiwicmVzaXplIjoiNDkweDEwMDAifSwicHVyIjoidmFyaWF0aW9uIn19--421ab82856ce4ced5bdaa37f96bcb9060315a4c7/9781449370787.png?disposition=inline) [(1 review)](https://open.umn.edu/opentextbooks/textbooks/think-bayes-bayesian-statistics-made-simple#Reviews) Allen B. Downey, Franklin W. Olin College of Engineering Copyright Year: 2012 ISBN 13: 9781449370787 Publisher: [Green Tea Press](http://greenteapress.com/wp/think-bayes/) Language: English ## Formats Available - [Online](https://open.umn.edu/opentextbooks/formats/337) - [PDF](https://open.umn.edu/opentextbooks/formats/338) - [LaTeX](https://open.umn.edu/opentextbooks/formats/1966) - [Hardcopy](https://open.umn.edu/opentextbooks/formats/1967) ## Conditions of Use ![Attribution-NonCommercial](https://open.umn.edu/assets/library/licenses/AttributionNonCommercial-b63ddd6b.webp) [Attribution-NonCommercial](http://creativecommons.org/licenses/) CC BY-NC ## Reviews [Learn more about reviews.](https://open.umn.edu/opentextbooks/reviews/rubric) Reviewed by Edwin Chong, Professor, Colorado State University on 12/5/16 The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods. [read more]() ## Table of Contents - Preface - 1 Bayes's Theorem - 2 Computational Statistics - 3 Estimation - 4 More Estimation - 5 Odds and Addends - 6 Decision Analysis - 7 Prediction - 8 Observer Bias - 9 Two Dimensions - 10 Approximate Bayesian Computation - 11 Hypothesis Testing - 12 Evidence - 13 Simulation - 14 A Hierarchical Model - 15 Dealing with Dimensions ## About the Book *Think Bayes* is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems. ### Author **Allen B. Downey** is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997. He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc. ## Ancillaries ### Homework - [Green Tea Press](https://open.umn.edu/opentextbooks/166/ancillaries/think-bayes-bayesian-statistics-made-simple) [Submit ancillary resource](https://open.umn.edu/opentextbooks/ancillaries/submit?textbook_id=288) ## Contribute to this Page [Suggest an edit to this book record](https://open.umn.edu/opentextbooks/textbooks/think-bayes-bayesian-statistics-made-simple/edit)

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