Ah great, the new book on Bayesian workflow by Andrew Gelman, Aki Vehtari, Richard McElreath I knew they were working on is about to appear! With entries from several coauthors and half of the chapters on case studies. I have not (yet) looked at its contents in detail…
Archive for Statistical Modeling
Bayesian Workflow [cover]
Posted in Books, pictures, R, Statistics, University life with tags Aki Vehtari, Andrew Gelman, Bayesian inference, Bayesian statistics, Bayesian workflow, calibration checking, case studies, Chapman & Hall, computational complexity, cover, CRC Press, decision making, model building, model checking, not a book review, R, Richard McElreath, STAN, Statistical Modeling, statistical practice, workflow on April 25, 2026 by xi'anstatistical modeling with R [book review]
Posted in Books, Statistics with tags AIC, Bayes factors, Bayesian Analysis, Bayesian data analysis, book review, brms, CHANCE, conjugate priors, Deborah Mayo, DIC, fitdist, fitistrplus, fonts, frequentist inference, Gibbs sampling, glm, glmer, JASA, Jeddah, Jeffreys priors, Journal of the American Statistical Association, machine learning, MCMC, Metropolis-Hastings algorithm, model misspecification, non-parametrics, Ockham's razor, OUP, Oxford University Press, packages, plagiarism, prior selection, R, STAN, Statistical Modeling, Steve Fienberg, support, Uruguay, WAIC on June 10, 2023 by xi'anStatistical Modeling with R (A dual frequentist and Bayesian approach for life scientists) is a recent book written by Pablo Inchausti, from Uruguay. In a highly personal and congenial style (witness the preface), with references to (fiction) books that enticed me to buy them. The book was sent to me by the JASA book editor for review and I went through the whole of it during my flight back from Jeddah. [Disclaimer about potential self-plagiarism: this post or a likely edited version of it will eventually appear in JASA. If not CHANCE, for once.]
The very first sentence (after the preface) quotes my late friend Steve Fienberg, which is definitely starting on the right foot. The exposition of the motivations for writing the book is quite convincing, with more emphasis than usual put on the notion and limitations of modeling. The discourse is overall inspirational and contains many relevant remarks and links that make it worth reading it as a whole. While heavily connected with a few R packages like fitdist, fitistrplus, brms (a front for Stan), glm, glmer, the book is wisely bypassing the perilous reef of recalling R bases. Similarly for the foundations of probability and statistics. While lacking in formal definitions, in my opinion, it reads well enough to somehow compensate for this very lack. I also appreciate the coherent and throughout continuation of the parallel description of Bayesian and non-Bayesian analyses, an attempt that often too often quickly disappear in other books. (As an aside, note that hardly anyone claims to be a frequentist, except maybe Deborah Mayo.) A new model is almost invariably backed by a new dataset, if a few being somewhat inappropriate as in the mammal sleep patterns of Chapter 5. Or in Fig. 6.1.
Given that the main motivation for the book (when compared with references like BDA) is heavily towards the practical implementation of statistical modelling via R packages, it is inevitable that a large fraction of Statistical Modeling with R is spent on the analysis of R outputs, even though it sometimes feels a wee bit too heavy for yours truly. The R screen-copies are however produced in moderate quantity and size, even though the variations in typography/fonts (at least on my copy?!) may prove confusing. Obviously the high (explosive?) distinction between regression models may eventually prove challenging for the novice reader. The specific issue of prior input (or “defining priors”) is briefly addressed in a non-chapter (p.323), although mentions are made throughout preceding chapters. I note the nice appearance of hierarchical models and experimental designs towards the end, but would have appreciated some discussions on missing topics such as time series, causality, connections with machine learning, non-parametrics, model misspecification. As an aside, I appreciated being reminded about the apocryphal nature of Ockham’s much cited quote “Pluralitas non est ponenda sine necessitate“.
Typo Jeffries found in Fig. 2.1, along with a rather sketchy representation of the history of both frequentist and Bayesian statistics. And Jon Wakefield’s book (with related purpose of presenting both versions of parametric inference) was mistakenly entered as Wakenfield’s in the bibliography file. Some repetitions occur. I do not like the use of the equivalence symbol ≈ for proportionality. And I found two occurrences of the unavoidable “the the” typo (p.174 and p.422). I also had trouble with some sentences like “long-run, hypothetical distribution of parameter estimates known as the sampling distribution” (p.27), “maximum likelihood estimates [being] sufficient” (p.28), “Jeffreys’ (1939) conjugate priors” [which were introduced by Raiffa and Schlaifer] (p.35), “A posteriori tests in frequentist models” (p.130), “exponential families [having] limited practical implications for non-statisticians” (p.190), “choice of priors being correct” (p.339), or calling MCMC sample terms “estimates” (p.42), and issues with some repetitions, missing indices for acronyms, packages, datasets, but did not bemoan the lack homework sections (beyond suggesting new datasets for analysis).
A problematic MCMC entry is found when calibrating the choice of the Metropolis-Hastings proposal towards avoiding negative values “that will generate an error when calculating the log-likelihood” (p.43) since it suggests proposed values should not exceed the support of the posterior (and indicates a poor coding of the log-likelihood!). I also find the motivation for the full conditional decomposition behind the Gibbs sampler (p.47) unnecessarily confusing. (And automatically having a Metropolis-Hastings step within Gibbs as on Fig. 3.9 brings another magnitude of confusion.) The Bayes factor section is very terse. The derivation of the Kullback-Leibler representation (7.3) as an expected log likelihood ratio seems to be missing a reference measure. Of course, seeing a detailed coverage of DIC (Section 7.4) did not suit me either, even though the issue with mixtures was alluded to (with no detail whatsoever). The Nelder presentation of the generalised linear models felt somewhat antiquated, since the addition of the scale factor a(φ) sounds over-parameterized.
But those are minor quibble in relation to a book that should attract curious minds of various background knowledge and expertise in statistics, as well as work nicely to support an enthusiastic teacher of statistical modelling. I thus recommend this book most enthusiastically.
urgent call for two scholarships for COVID-19 research at the University of Insubria, Como, Italy
Posted in Statistics, Travel, University life with tags Como, COVID-19, Italy, Lago di Como, research position, Statistical Modeling, Università di Insubria on August 13, 2020 by xi'an
My friend Antonietta Mira sent me this urgent call for two short-term research positions at her Italian university, in Como, Lombardia, Italy. The deadline is 25 August, 2020! (Please send any enquiry to her, not to me!)
Professor Antonietta Mira has received financial support for two scholarships, one for 6 and the other one for 9 months, for research related to COVID-19.
The gross monthly salary is 2666 Euros per month. Most of the research can be conducted remotely. The starting date of the scholarships will be approximately the first of October 2020.
The application deadline is 25/08/2020 at noon. The two calls only differ by the duration of the position (6 months or 9 months). Interested candidates should submit one application for each of the two calls unless they have a specific preference for one of the two durations.
Applications should be sent by e-mail to segreteria.dipsat[AT]uninsubria.it with scanned handwritten signature and a copy of the identity card.
We are looking for is a brilliant researcher ideally with a doctorate in statistics or related subjects, autonomous in data analysis and estimation of models for forecasting. The researcher will collaborate with a research team with interdisciplinary competences. The research aims to predict the impact of the COVID-19 Wave on the Emergency and Urgency System in Lombardy.
If their PhD is not completed the applicants should finish their thesis by the end of the year or consider the research conducted under the fellowship as part of the research for the doctoral thesis.
The call is only available in Italian. Please note original documents and official translations are not needed when submitting the application. They will be only needed in case the applicant becomes the selected candidate for the position
visual effects
Posted in Books, pictures, Statistics with tags Bayesian inference, Cardiff, concrete shoes, data visualisation, fudge, Journal of the Royal Statistical Society, leave-one-out calibration, noninformative priors, Royal Statistical Society, RSS, Series A, Statistical Modeling on November 2, 2018 by xi'an
As advertised and re-discussed by Dan Simpson on the Statistical Modeling, &tc. blog he shares with Andrew and a few others, the paper Visualization in Bayesian workflow he wrote with Jonah Gabry, Aki Vehtari, Michael Betancourt and Andrew Gelman was one of three discussed at the RSS conference in Cardiff, last week month, as a Read Paper for Series A. I had stored the paper when it came out towards reading and discussing it, but as often this good intention led to no concrete ending. [Except concrete as in concrete shoes…] Hence a few notes rather than a discussion in Series B A.
Exploratory data analysis goes beyond just plotting the data, which should sound reasonable to all modeling readers.
Fake data [not fake news!] can be almost [more!] as valuable as real data for building your model, oh yes!, this is the message I am always trying to convey to my first year students, when arguing about the connection between models and simulation, as well as a defense of ABC methods. And more globally of the very idea of statistical modelling. While indeed “Bayesian models with proper priors are generative models”, I am not particularly fan of using the prior predictive [or the evidence] to assess the prior as it may end up in a classification of more or less all but terrible priors, meaning that all give very little weight to neighbourhoods of high likelihood values. Still, in a discussion of a TAS paper by Seaman et al. on the role of prior, Kaniav Kamary and I produced prior assessments that were similar to the comparison illustrated in Figure 4. (And this makes me wondering which point we missed in this discussion, according to Dan.) Unhappy am I with the weakly informative prior illustration (and concept) as the amount of fudging and calibrating to move from the immensely vague choice of N(0,100) to the fairly tight choice of N(0,1) or N(1,1) is not provided. The paper reads like these priors were the obvious and first choice of the authors. I completely agree with the warning that “the utility of the the prior predictive distribution to evaluate the model does not extend to utility in selecting between models”.
MCMC diagnostics, beyond trace plots, yes again, but this recommendation sounds a wee bit outdated. (As our 1998 reviewww!) Figure 5(b) links different parameters of the model with lines, which does not clearly relate to a better understanding of convergence. Figure 5(a) does not tell much either since the green (divergent) dots stand within the black dots, at least in the projected 2D plot (and how can one reach beyond 2D?) Feels like I need to rtfm..!
“Posterior predictive checks are vital for model evaluation”, to wit that I find Figure 6 much more to my liking and closer to my practice. There could have been a reference to Ratmann et al. for ABC where graphical measures of discrepancy were used in conjunction with ABC output as direct tools for model assessment and comparison. Essentially predicting a zero error with the ABC posterior predictive. And of course “posterior predictive checking makes use of the data twice, once for the fitting and once for the checking.” Which means one should either resort to loo solutions (as mentioned in the paper) or call for calibration of the double-use by re-simulating pseudo-datasets from the posterior predictive. I find the suggestion that “it is a good idea to choose statistics that are orthogonal to the model parameters” somewhat antiquated, in that this sounds like rephrasing the primeval call to ancillary statistics for model assessment (Kiefer, 1975), while pretty hard to implement in modern complex models.
Statistical modeling and computation [apologies]
Posted in Books, R, Statistics, University life with tags apologies, Australia, Bayesian statistics, Dirk Kroese, introductory textbooks, Joshua Chan, Monte Carlo methods, Monte Carlo Statistical Methods, R, state space model, Statistical Modeling, typo on June 11, 2014 by xi'an
In my book review of the recent book by Dirk Kroese and Joshua Chan, Statistical Modeling and Computation, I mistakenly and persistently typed the name of the second author as Joshua Chen. This typo alas made it to the printed and on-line versions of the subsequent CHANCE 27(2) column. I am thus very much sorry for this mistake of mine and most sincerely apologise to the authors. Indeed, it always annoys me to have my name mistyped (usually as Roberts!) in references. [If nothing else, this typo signals it is high time for a change of my prescription glasses.]
