## Archive for Bayes theorem

## baseless!

Posted in Books, Statistics with tags 1922, Bayes theorem, epistemic probability, frequency properties, George Boole, history of statistics, inverse probability, normalised maximum likelihood, Pierre Simon Laplace, R.A. Fisher, Siméon Poisson on July 13, 2021 by xi'an## Bayesian basics in Le Monde

Posted in Statistics with tags Bayes theorem, causality, COVID-19, False positive, Le Monde, medical statistics, pandemic on September 12, 2020 by xi'an## Bayes plaque

Posted in Books, pictures, Statistics, Travel, University life with tags Bayes theorem, Edinburgh, FRS, plaque, Royal Society, Scotland, Thomas Bayes, University of Edinburgh on November 22, 2019 by xi'an## a hatchet job [book review]

Posted in Books, Statistics, University life with tags Bayes theorem, Bayesian statistics, betting, book review, Bruce Hill, Bruno de Finetti, JASA, John Hartigan, Likelihood Principle on July 20, 2019 by xi'an**B**y happenstance, I came across a rather savage review of John Hartigan’s Bayes Theory (1984) written by Bruce Hill in HASA, including the following slivers:

“By and large this book is at its best in developing the mathematical consequences of the theory and at its worst when dealing with the underlying ideas and concepts, which seems unfortunate since Bayesian statistics is above all an attempt to deal realistically with the nature of uncertainty and decision making.” B. Hill, JASA, 1986, p.569

“Unfortunately, those who had hoped for a serious contribution to the question will be disappointed.” B. Hill, JASA, 1986, p.569

“If the primary concern is mathematical convenience, not content or meaning, then the enterprise is a very different matter from what most of us think of as Bayesian approach.” B. Hill, JASA, 1986, p.570

“Perhaps in a century or two statisticians and probabilists will reach a similar state of maturity.” B. Hill, JASA, 1986, p.570“

Perhaps this is a good place to mention that the notation in the book is formidable. Bayes’s theorem appears in a form that is almost unrecognizable. As elsewhere, the mathematical treatment is elegant. but none of the deeper issues about the meaning and interpretation of conditional probability is discussed.” B. Hill, JASA, 1986, p.570

“The reader will find many intriguing ideas, much that is outrageous, and even some surprises (the likelihood principle is not mentioned, and conditional inference is just barely mentioned).” B. Hill, JASA, 1986, p.571

“What is disappointing to me is that with a little more discipline and effort with regard to the ideas underlying Bayesian statistics, this book could have been a major contribution to the theory.” B. Hill, JASA, 1986, p.571

Another review by William Sudderth (1985, Bulletin of the American Mathematical Society) is much kinder to the book, except for the complaint that “the pace is brisk and sometimes hard to follow”.

## Statistical rethinking [book review]

Posted in Books, Kids, R, Statistics, University life with tags Amazon, Bayes theorem, Bayesian data analysis, Bayesian Essentials with R, book review, CHANCE, code, convergence diagnostics, E.T. Jaynes, generalised linear models, golem, maths, matrix algebra, MCMC algorithms, mixtures of distributions, Monte Carlo Statistical Methods, Prague, R, robots, STAN, statistical modelling, Statistical rethinking on April 6, 2016 by xi'anStatistical Rethinking: A Bayesian Course with Examples in R and Stan is a new book by Richard McElreath that CRC Press sent me for review in CHANCE. While the book was already discussed on Andrew’s blog three months ago, and [rightly so!] enthusiastically recommended by Rasmus Bååth on Amazon, here are the reasons why I am quite impressed by Statistical Rethinking!

“Make no mistake: you will wreck Prague eventually.” (p.10)

While the book has a lot in common with Bayesian Data Analysis, from being in the same CRC series to adopting a pragmatic and weakly informative approach to Bayesian analysis, to supporting the use of STAN, it also nicely develops its own ecosystem and idiosyncrasies, with a noticeable Jaynesian bent. To start with, I like the highly personal style with clear attempts to make the concepts memorable for students by resorting to external concepts. The best example is the call to the myth of the golem in the first chapter, which McElreath uses as an warning for the use of statistical models (which almost are anagrams to golems!). Golems and models [and robots, another concept invented in Prague!] are man-made devices that strive to accomplish the goal set to them without heeding the consequences of their actions. This first chapter of Statistical Rethinking is setting the ground for the rest of the book and gets quite philosophical (albeit in a readable way!) as a result. In particular, there is a most coherent call against hypothesis testing, which by itself justifies the title of the book. Continue reading