MARC details
| 000 -LEADER |
| fixed length control field |
02439nam a2200349 4500 |
| 001 - CONTROL NUMBER |
| control field |
OTLid0000288 |
| 003 - CONTROL NUMBER IDENTIFIER |
| control field |
MnU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
| control field |
20201105133313.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
| fixed length control field |
m o d s |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
| fixed length control field |
180907s2012 mnu o 0 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
| International Standard Book Number |
9781449370787 |
| 040 ## - CATALOGING SOURCE |
| Original cataloging agency |
MnU |
| Language of cataloging |
eng |
| Transcribing agency |
MnU |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
| Classification number |
QA76 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
| Personal name |
Downey, Allen B. |
| Relator term |
author |
| 245 00 - TITLE STATEMENT |
| Title |
Think Bayes |
| Remainder of title |
Bayesian Statistics Made Simple |
| Statement of responsibility, etc. |
Allen Downey |
| 264 #2 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Name of producer, publisher, distributor, manufacturer |
Open Textbook Library |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Name of producer, publisher, distributor, manufacturer |
Green Tea Press |
| 300 ## - PHYSICAL DESCRIPTION |
| Extent |
1 online resource |
| 490 0# - SERIES STATEMENT |
| Series statement |
Open textbook library. |
| 505 0# - FORMATTED CONTENTS NOTE |
| Formatted contents note |
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 |
| 520 0# - SUMMARY, ETC. |
| Summary, etc. |
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. |
| 542 1# - INFORMATION RELATING TO COPYRIGHT STATUS |
| Copyright statement |
Attribution-NonCommercial |
| 546 ## - LANGUAGE NOTE |
| Language note |
In English. |
| 588 0# - SOURCE OF DESCRIPTION NOTE |
| Source of description note |
Description based on online resource |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name entry element |
Computer Science |
| Form subdivision |
Textbooks |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
| Corporate name or jurisdiction name as entry element |
Open Textbook Library |
| Relator term |
distributor |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
| Uniform Resource Identifier |
<a href="https://open.umn.edu/opentextbooks/textbooks/288">https://open.umn.edu/opentextbooks/textbooks/288</a> |
| Public note |
Access online version |