**A** tribune in the NYT of yesterday on the importance of being Bayesian. When an epidemiologist. Tribune that was forwarded to me by a few friends (and which I missed on my addictive monitoring of the journal!). It is written by , a Canadian journalist writing about mathematics (and obviously statistics). And it brings to the general public the main motivation for adopting a Bayesian approach, namely its coherent handling of uncertainty and its ability to update in the face of new information. (Although it might be noted that other flavours of statistical analysis are also able to update their conclusions when given more data.) The COVID situation is a perfect case study in Bayesianism, in that there are so many levels of uncertainty and imprecision, from the models themselves, to the data, to the outcome of the tests, &tc. The article is journalisty, of course, but it quotes from a range of statisticians and epidemiologists, including Susan Holmes, whom I learned was quarantined 105 days in rural Portugal!, developing a hierarchical Bayes modelling of the prevalent SEIR model, and David Spiegelhalter, discussing Cromwell’s Law (or better, humility law, for avoiding the reference to a fanatic and tyrannic Puritan who put Ireland to fire and the sword!, and had in fact very little humility for himself). Reading the comments is both hilarious (it does not take long to reach the point when Trump is mentioned, and Taleb’s stance on models and tails makes an appearance) and revealing, as many readers do not understand the meaning of Bayes’ inversion between causes and effects, or even the meaning of Jeffreys’ bar, |, as conditioning.

## Archive for Thomas Bayes

## Bayes @ NYT

Posted in Books, Kids, Statistics, University life with tags Alan Turing, applied Bayesian analysis, COVID-19, hierarchical Bayesian modelling, Ireland, journalism, NYT, Oliver Cromwell, The Bayesian Choice, The New York Times, Thomas Bayes, vulgarisation on August 8, 2020 by xi'an## Monte Carlo Markov chains

Posted in Books, Statistics, University life with tags Andrei Kolmogorov, Bayesian Analysis, Bayesian model comparison, book review, CHANCE, Gregor Mendel, iris data, irreducibility, JASA, Jeffreys priors, Kolmogorov axioms, Kolmogorov-Smirnov distance, MCMC, physics, population genetics, pot-pourri, recurrence, Richard Price, Ronald Fisher, Springer-Verlag, textbook, Thomas Bayes, W. Gosset on May 12, 2020 by xi'an**D**arren Wraith pointed out this (currently free access) Springer book by Massimiliano Bonamente [whose family name means *good spirit* in Italian] to me for its use of the unusual *Monte Carlo Markov chain* rendering of MCMC. (Google Trend seems to restrict its use to California!) This is a graduate text for physicists, but one could nonetheless expect more rigour in the processing of the topics. Particularly of the Bayesian topics. Here is a pot-pourri of memorable quotes:

*“Two major avenues are available for the assignment of probabilities. One is based on the repetition of the experiments a large number of times under the same conditions, and goes under the name of the frequentist or classical method. The other is based on a more theoretical knowledge of the experiment, but without the experimental requirement, and is referred to as the Bayesian approach.”*

*“The Bayesian probability is assigned based on a quantitative understanding of the nature of the experiment, and in accord with the Kolmogorov axioms. It is sometimes referred to as *empirical probability*, in recognition of the fact that sometimes the probability of an event is assigned based upon a practical knowledge of the experiment, although without the classical requirement of repeating the experiment for a large number of times. This method is named after the Rev. Thomas Bayes, who pioneered the development of the theory of probability.”*

*“The likelihood P(B/A) represents the probability of making the measurement B given that the model A is a correct description of the experiment.”*

*“…a uniform distribution is normally the logical assumption in the absence of other information.”*

*“The Gaussian distribution can be considered as a special case of the binomial, when the number of tries is sufficiently large.”*

*“This clearly does not mean that the Poisson distribution has no variance—in that case, it would not be a random variable!”*

*“The method of moments therefore returns unbiased estimates for the mean and variance of every distribution in the case of a large number of measurements.”*

*“The great advantage of the Gibbs sampler is the fact that the acceptance is 100 %, since there is no rejection of candidates for the Markov chain, unlike the case of the Metropolis–Hastings algorithm.”*

Let me then point out (or just whine about!) the book using “statistical independence” for plain independence, the use of / rather than Jeffreys’ | for conditioning (and sometimes forgetting \ in some LaTeX formulas), the confusion between events and random variables, esp. when computing the posterior distribution, between models and parameter values, the reliance on discrete probability for continuous settings, as in the Markov chain chapter, confusing density and probability, using Mendel’s pea data without mentioning the unlikely fit to the expected values (or, as put more subtly by Fisher (1936), “the data of most, if not all, of the experiments have been falsified so as to agree closely with Mendel’s expectations”), presenting Fisher’s and Anderson’s *Iris data* [a motive for rejection when George was JASA editor!] as a “a new classic experiment”, mentioning Pearson but not Lee for the data in the 1903 Biometrika paper “On the laws of inheritance in man” (and woman!), and not accounting for the discrete nature of this data in the linear regression chapter, the three page derivation of the Gaussian distribution from a Taylor expansion of the Binomial pmf obtained by differentiating in the integer argument, spending endless pages on deriving standard properties of classical distributions, this appalling mess of adding over the conditioning atoms with no normalisation in a Poisson experiment

,

botching the proof of the CLT, which is treated *before* the Law of Large Numbers, restricting maximum likelihood estimation to the Gaussian and Poisson cases and muddling its meaning by discussing unbiasedness, confusing a drifted Poisson random variable with a drift on its parameter, as well as using the pmf of the Poisson to define an area under the curve (Fig. 5.2), sweeping the improperty of a constant prior under the carpet, defining a null hypothesis as a range of values for a summary statistic, no mention of Bayesian perspectives in the hypothesis testing, model comparison, and regression chapters, having one-dimensional case chapters followed by two-dimensional case chapters, reducing model comparison to the use of the Kolmogorov-Smirnov test, processing bootstrap and jackknife in the Monte Carlo chapter without a mention of importance sampling, stating recurrence results without assuming irreducibility, motivating MCMC by the intractability of the evidence, resorting to the term *link* to designate the current value of a Markov *chain*, incorporating the need for a prior distribution in a terrible description of the Metropolis-Hastings algorithm, including a discrete proof for its stationarity, spending many pages on early 1990’s MCMC convergence tests rather than discussing the adaptive scaling of proposal distributions, the inclusion of numerical tables [in a 2017 book] and turning Bayes (1763) into Bayes and Price (1763), or Student (1908) into Gosset (1908).

*[Usual disclaimer about potential self-plagiarism: this post or an edited version of it could possibly appear later in my Books Review section in CHANCE. Unlikely, though!]*

## Computing Bayes: Bayesian Computation from 1763 to the 21st Century

Posted in Books, pictures, Statistics, Travel, University life with tags 1763, Australia, Bayes on the Beach, Bayesian computation, Monash University, survey, Thomas Bayes on April 16, 2020 by xi'an**L**ast night, Gael Martin, David Frazier (from Monash U) and myself arXived a survey on the history of Bayesian computations. This project started when Gael presented a historical overview of Bayesian computation, then entitled ‘Computing Bayes: Bayesian Computation from 1763 to 2017!’, at ‘Bayes on the Beach’ (Queensland, November, 2017). She then decided to build a survey from the material she had gathered, with her usual dedication and stamina. Asking David and I to join forces and bring additional perspectives on this history. While this is a short and hence necessary incomplete history (of not everything!), it hopefully brings some different threads together in an original enough fashion (as I think there is little overlap with recent surveys I wrote). We welcome comments about aspects we missed, skipped or misrepresented, most obviously!