Bayesian statistics for beginners: a step-by-step approach

By:

Donovan, Therese M

Contributor(s):

Mickey, Ruth M

Material type: Text

TextPublication details: Oxford University Press Oxford 2019Description: x, 419 pISBN:

9780198841302

Subject(s):

DDC classification:

519.542 DON

Summary: Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices. (https://academic.oup.com/book/41940)

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.542 DON (Browse shelf(Opens below))	1	Available		006225

Browsing Indian Institute of Management LRC shelves, Shelving location: General Stacks, Collection: Operations Management & Quantitative Techniques Close shelf browser (Hides shelf browser)

Previous								Next
Previous	519.54 AIT Introduction to statistical modelling and inference	519.54 KIM Statistical methods for handling incomplete data	519.54 MAV Probability and statistical inference:	519.542 DON Bayesian statistics for beginners: a step-by-step approach	519.542 LIO Bayesian inference and computation in reliability and survival analysis	519.542 LON Statistics for making decisions	519.542 WAT Mathematical theory of bayesian statistics	Next

Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices.

(https://academic.oup.com/book/41940)

There are no comments on this title.

to post a comment.