I'm writing a book about Bayesian statistics, decision theory, information theory, and connected topics. It's coming along slowly, but I thought I'd share a bit of a preview. This is an early draft of the first 11 pages, including the title, a table of contents, and a few pages of writing. Much will change I'm sure. Any purchases will be considered encouragement for me to actually finish.

Albie (our pet Alexandrine parakeet) opening his foraging toy by turning the keys. Mmm, seed.

I'm writing a book about Bayesian statistics, decision theory, information theory, and connected topics. It's coming along slowly, but I thought I'd share a bit of a preview. This is an early draft of the first 11 pages, including the title, a table of contents, and a few pages of writing. Much will change I'm sure. Any purchases will be considered encouragement for me to actually finish.

These are the lectures for the 2017 edition of "STATS 331: Introduction to Bayesian Statistics", taught by Brendon J. Brewer at the University of Auckland. This is an undergraduate course with minimal prerequisites. Some exposure to probability theory, data analysis, and calculus is beneficial. The total running time is 18 hours and 53 minutes, and the file size is 2.57 GiB. The coursebook is at https://www.stat.auckland.ac.nz/~brewer/stats331.pdf. The approximate starting times of the individual lectures are: Lecture 1 - Introduction - 00:00:00 Lecture 2 - Maths, Stats, and R - 00:48:50 Lecture 3 - First Examples - 01:42:00 Lecture 4 - Bigger Bayes Boxes - 02:33:10 Lecture 5 - Parameter Estimation - 03:25:20 (see also https://www.stat.auckland.ac.nz/~brewer/taxis.R) Lecture 6 - More Parameter Estimation - 04:19:40 (see also https://www.stat.auckland.ac.nz/~brewer/volcanoes.R) Lecture 7 - Prediction, Analytic Methods - 05:11:10 Lecture 8 - Analytical Methods, Hypothesis Testing - 05:57:20 Lecture 9 - Model Selection - 06:51:30 Lecture 10 - Summaries and Decisions - 07:44:50 Lecture 11 - Intervals, Motivating MCMC - 08:35:00 Lecture 12 - The Metropolis Algorithm - 09:28:55 Lecture 13 - JAGS - 10:24:30 Lecture 14 - More JAGS - 11:16:00 Lecture 15 - Sports Prediction - 12:08:50 Lecture 16 - Linear Regression - 13:02:05 Lecture 17 - Outliers, Multiple Regression - 13:54:15 Lecture 18 - T-Test Type Models - 14:48:40 Lecture 19 - ANOVA Model, Posterior Predictive Checks - 15:39:15 Lecture 20 - Time Series / AR(1) - 16:31:00 Lecture 21 - Dirichlet / Multinomial - 17:14:55 Lecture 22 - Conclusion - 18:01:00 Tip jars: lbry: bDxomVYFm5CCW6XdopH55jLgzyqW7D1DvS ETH: 0xC5d3838417A96243263fE1FC7092b13c2e226D40 LTC: LYrq48bFxpbC678xjbFVPuzCLRrWErW8uM