## Le Monde puzzle [#1133]

Posted in Books, Kids, R with tags , , , , , , on March 28, 2020 by xi'an

A weekly Monde current mathematical puzzle that reminded me of an earlier one (but was too lazy to check):

If ADULE-ELUDE=POINT, was is the largest possible value of POINT? With the convention that all letters correspond to different digits and no digit can start with 0. Same question when ADULE+ELUDE=POINT.

The run of a brute force R search return 65934 as the solution (codegolf welcomed!)

dify<-function(aluda,point)
(sum(aluda*10^(4:0))-sum(rev(aluda)*10^(4:0)))
num2dig<-function(dif) (dif%/%10^(0:4))%%10
sl=NULL
for (t in 1:1e6){
sl=as.matrix(distinct(as.data.frame(sl),.keep_all = TRUE))


where distinct is a dplyr R function.

> 94581-18549
[1] 76032


The code can be easily turned into solving the second question

> 31782+28713
[1] 60495


## coronavirus also hits reproduction rights!

Posted in Kids with tags , , , , , , , , , , , on March 27, 2020 by xi'an

“The [UK] Department of Health says reported changes to the abortion law, that would allow women to take both pills at home during the coronavirus outbreak, are not going ahead.” Independent, 23 March

“Texas and Ohio have included abortions among the nonessential surgeries and medical procedures that they are requiring to be delayed, saying they are trying to preserve precious protective equipment for health care workers and to make space for a potential flood of coronavirus patients.” The New York Times, 23 March

“Le ministre de la Santé, Olivier Véran, et la secrétaire d’Etat chargée de l’Égalité femmes-hommes, Marlène Schiappa, ont tenté lundi de rassurer : les IVG « sont considérées comme des interventions urgentes », et leur « continuité doit être assurée ». Le gouvernement veillera à ce que « le droit des femmes à disposer de leur corps ne soit pas remis en cause », ont-ils assuré.” Le Parisien, 23 March

“Lawmakers voted on Wednesday to liberalize New Zealand’s abortion law and allow unrestricted access during the first half of pregnancy, ending the country’s status as one of the few wealthy nations to limit the grounds for abortion during that period.” The New York Times, 18 March

## deduplication and population size estimation [discussion opened]

Posted in Books, pictures, Running, Statistics, University life with tags , , , , on March 27, 2020 by xi'an

A call (worth disseminating) for discussions on the paper “A Unified Framework for De-Duplication and Population Size Estimation” by [my friends] Andrea Tancredi, Rebecca Steorts, and Brunero Liseo, to appear on the June 2020 issue of Bayesian Analysis. The deadline is 24 April.

Data de-duplication is the process of detecting records in one or more datasets which refer to the same entity. In this paper we tackle the de-duplication process via a latent entity model, where the observed data are perturbed versions of a set of key variables drawn from a finite population of N different entities. The main novelty of our approach is to consider the population size N as an unknown model parameter. As a result, a salient feature of the proposed method is the capability of the model to account for the de-duplication uncertainty in the population size estimation. As by-products of our approach we illustrate the relationships between de-duplication problems and capture-recapture models and we obtain a more adequate prior distribution on the linkage structure. Moreover we propose a novel simulation algorithm for the posterior distribution of the matching configuration based on the marginalization of the key variables at population level. We apply our method to two synthetic data sets comprising German names. In addition we illustrate a real data application, where we match records from two lists which report information about people killed in the recent Syrian conflict.

## hanami [at home]

Posted in Statistics with tags , , , , , , , , , on March 26, 2020 by xi'an

## Probability and Bayesian modeling [book review]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , on March 26, 2020 by xi'an

Probability and Bayesian modeling is a textbook by Jim Albert [whose reply is included at the end of this entry] and Jingchen Hu that CRC Press sent me for review in CHANCE. (The book is also freely available in bookdown format.) The level of the textbook is definitely most introductory as it dedicates its first half on probability concepts (with no measure theory involved), meaning mostly focusing on counting and finite sample space models. The second half moves to Bayesian inference(s) with a strong reliance on JAGS for the processing of more realistic models. And R vignettes for the simplest cases (where I discovered R commands I ignored, like dplyr::mutate()!).

As a preliminary warning about my biases, I am always reserved at mixing introductions to probability theory and to (Bayesian) statistics in the same book, as I feel they should be separated to avoid confusion. As for instance between histograms and densities, or between (theoretical) expectation and (empirical) mean. I therefore fail to relate to the pace and tone adopted in the book which, in my opinion, seems to dally on overly simple examples [far too often concerned with food or baseball] while skipping over the concepts and background theory. For instance, introducing the concept of subjective probability as early as page 6 is laudable but I doubt it will engage fresh readers when describing it as a measurement of one’s “belief about the truth of an event”, then stressing that “make any kind of measurement, one needs a tool like a scale or ruler”. Overall, I have no particularly focused criticisms on the probability part except for the discrete vs continuous imbalance. (With the Poisson distribution not covered in the Discrete Distributions chapter. And the “bell curve” making a weird and unrigorous appearance there.) Galton’s board (no mention found of quincunx) could have been better exploited towards the physical definition of a prior, following Steve Stiegler’s analysis, by adding a second level. Or turned into an R coding exercise. In the continuous distributions chapter, I would have seen the cdf coming first to the pdf, rather than the opposite. And disliked the notion that a Normal distribution was supported by an histogram of (marathon) running times, i.e. values lower bounded by 122 (at the moment). Or later (in Chapter 8) for Roger Federer’s serving times. Incidentally, a fun typo on p.191, at least fun for LaTeX users, as

$f_{Y\ mid X}$

with an extra space between \’ and mid’! (I also noticed several occurrences of the unvoidable “the the” typo in the last chapters.) The simulation from a bivariate Normal distribution hidden behind a customised R function sim_binom() when it could have been easily described as a two-stage hierarchy. And no comment on the fact that a sample from Y-1.5X could be directly derived from the joint sample. (Too unconscious a statistician?)

When moving to Bayesian inference, a large section is spent on very simple models like estimating a proportion or a mean, covering both discrete and continuous priors. And strongly focusing on conjugate priors despite giving warnings that they do not necessarily reflect prior information or prior belief. With some debatable recommendation for “large” prior variances as weakly informative or (worse) for Exp(1) as a reference prior for sample precision in the linear model (p.415). But also covering Bayesian model checking either via prior predictive (hence Bayes factors) or posterior predictive (with no mention of using the data twice). A very marginalia in introducing a sufficient statistic for the Normal model. In the Normal model checking section, an estimate of the posterior density of the mean is used without (apparent) explanation.

“It is interesting to note the strong negative correlation in these parameters. If one assigned informative independent priors on and , these prior beliefs would be counter to the correlation between the two parameters observed in the data.”

For the same reasons of having to cut on mathematical validation and rigour, Chapter 9 on MCMC is not explaining why MCMC algorithms are converging outside of the finite state space case. The proposal in the algorithmic representation is chosen as a Uniform one, since larger dimension problems are handled by either Gibbs or JAGS. The recommendations about running MCMC do not include how many iterations one “should” run (or other common queries on Stack eXchange), albeit they do include the sensible running multiple chains and comparing simulated predictive samples with the actual data as a  model check. However, the MCMC chapter very quickly and inevitably turns into commented JAGS code. Which I presume would require more from the students than just reading the available code. Like JAGS manual. Chapter 10 is mostly a series of examples of Bayesian hierarchical modeling, with illustrations of the shrinkage effect like the one on the book cover. Chapter 11 covers simple linear regression with some mentions of weakly informative priors,  although in a BUGS spirit of using large [enough?!] variances: “If one has little information about the location of a regression parameter, then the choice of the prior guess is not that important and one chooses a large value for the prior standard deviation . So the regression intercept and slope are each assigned a Normal prior with a mean of 0 and standard deviation equal to the large value of 100.” (p.415). Regardless of the scale of y? Standardisation is covered later in the chapter (with the use of the R function scale()) as part of constructing more informative priors, although this sounds more like data-dependent priors to me in the sense that the scale and location are summarily estimated by empirical means from the data. The above quote also strikes me as potentially confusing to the students, as it does not spell at all how to design a joint distribution on the linear regression coefficients that translate the concentration of these coefficients along y̅=β⁰+β¹x̄. Chapter 12 expands the setting to multiple regression and generalised linear models, mostly consisting of examples. It however suggests using cross-validation for model checking and then advocates DIC (deviance information criterion) as “to approximate a model’s out-of-sample predictive performance” (p.463). If only because it is covered in JAGS, the definition of the criterion being relegated to the last page of the book. Chapter 13 concludes with two case studies, the (often used) Federalist Papers analysis and a baseball career hierarchical model. Which may sound far-reaching considering the modest prerequisites the book started with.

In conclusion of this rambling [lazy Sunday] review, this is not a textbook I would have the opportunity to use in Paris-Dauphine but I can easily conceive its adoption for students with limited maths exposure. As such it offers a decent entry to the use of Bayesian modelling, supported by a specific software (JAGS), and rightly stresses the call to model checking and comparison with pseudo-observations. Provided the course is reinforced with a fair amount of computer labs and projects, the book can indeed achieve to properly introduce students to Bayesian thinking. Hopefully leading them to seek more advanced courses on the topic.

Update: Jim Albert sent me the following precisions after this review got on-line:

## BrewDog punk sanitiser

Posted in pictures, Wines with tags , , , , , , , , , , on March 25, 2020 by xi'an

## ensemble rejection sampling

Posted in Statistics with tags , , , on March 25, 2020 by xi'an

George Deligiannidis, Arnaud Doucet and Sylvain Rubenthaler have constructed a form of Rao-Blackwellised estimate out of a regular rejection sampler. Doubly surprisingly as turning importance sampling into regular sampling plus  gaining over the standard accept-reject estimate. They call their approach ensemble rejection sampling. This is done by seeing the N-sample created from the proposal as an importance sampler, exploiting the importance weights towards estimating the (intractable) normalising constant of the target density, and creating an upper bound on this estimate Ẑ. That depends on the current value X from the N-sample under consideration for acceptance as

Z⁺=Ẑ+{max(w)-w(X)}/N

with a probability Ẑ/Z⁺ to accept X. The amazing result is that the X thus marginaly produced is distributed from the target! Meaning that this is a case for a self-normalised importance sampling distribution producing an exact simulation from the target. While this cannot produce an iid sample, it can be exploited to produce unbiased estimators of expectations under the target. Without even resampling and at a linear cost in the sample size N.

The method can be extended to the dynamic (state-space) case. At a cost of O(N²T) as first observed by Radford Neal. However, the importance sample seems to be distributed from a product of proposals that do not account for the previous particles. But maybe accounting for the observations. While the result involves upper bounds on the dynamic importance weights, the capacity to deliver exact simulations remains a major achievement, in my opinion.