Logo

Think Bayes: Bayesian Statistics Made Simple

Small book cover: Think Bayes: Bayesian Statistics Made Simple

Think Bayes: Bayesian Statistics Made Simple
by

Publisher: Green Tea Press
Number of pages: 77

Description:
Think Bayes is an introduction to Bayesian statistics using computational methods. Contents: Bayes's Theorem; Computational statistics; Tanks and Trains; Urns and Coins; Odds and addends; Hockey; The variability hypothesis; Hypothesis testing.

Home page url

Download or read it online for free here:
Download link
(350KB, PDF)

Similar books

Book cover: Statistical Inference for EveryoneStatistical Inference for Everyone
by - Save The Broccoli Publishing
This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester.
(7819 views)
Book cover: A First Course In Mathematical StatisticsA First Course In Mathematical Statistics
by - Cambridge University Press
This book provides the mathematical foundations of statistics. It explains the principles, and proves the formulae to give validity to the methods of the interpretation of statistical data. It is of interest to students of a wide variety of subjects.
(10774 views)
Book cover: Statistical Treatment of Experimental DataStatistical Treatment of Experimental Data
by - McGraw Hill
A concise, highly readable introduction to statistical methods. Even with a limited mathematics background, readers can understand what statistical methods are and how they may be used to obtain the best possible results from experimental data.
(13763 views)
Book cover: Computer Age Statistical Inference: Algorithms, Evidence, and Data ScienceComputer Age Statistical Inference: Algorithms, Evidence, and Data Science
by - Stanford University
Beginning with classical inferential theories, the book takes up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, etc.
(4979 views)