Archive for OUP

renewable energy [book review]

Posted in Books, Kids, Travel with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on December 28, 2025 by xi'an

Renewable Energy (2nd edition, 2025), by Nick Jelley is part of the terrific “Very Short Introduction” series, which provides an expert introduction to a topic within 150 pages. (The OUP equivalent of the French series “Que sais-je?” that started in 1941.) The reason for the second edition, as provided by the author, is the “dramatic expansion, and fall in cost” of clean energy products. Unfortunately, it comes out just short of realising the magnitude of the backlash again renewable energy and fighting climate change launched by the second Trump administration and the ensuing added pressure on other countries to reduce further their fuel consumption.

The main sources of renewables are identified as wind, sun, and water, in a first chapter that operates as an historical recap on the evolution of energy sources and consumption. The second chapter stresses the need for renewable to fight global warming and to reverse climate change, with a rather vague discussion of the costs of producing energy from renewable. Chapter 3 focusses on (debatable) biomass, solar heat, and hydropower. Chapter 4 on wind power, with a few paragraphs on the production costs and the reluctance of local populations (that seems to be fuelled by right-wing parties). Chapter 5 is specifically about solar photovoltaïcs, deemed to be now cheaper than fossil fuels inmost countries. And substituting for deficient or inexistent large scale energy grids in some countries. And Chapter 6 deals (briefly) with other low-carbon technologies, like tidal dams (mentioning the 1966 La Rance dam near Mont Saint-Michel, Normandy, we would visit now and then when I was a kid!), wave turbines, nuclear energy, and geothermal power (both heating and providing electricity). Chapter 7 discusses renewable electricity issues with energy storage (batteries and pumped hydro storage), since most solutions cannot be fired at will. The book  addresses neither the loss in carrying electricity over long distances (as suggested p103 between Morocco and Europe, or Australia and Singapore), nor the hacking risks impacting large electricity grids. Chapter 8 switches to decarbonasing heat and transport, where heat pumps and electric vehicles are the most promising venues. Chapter 9 concludes by a more political discussion of the transition to renewable, pointing out the Chinese leadership in switching to solar and wind capacities. And the brake put on the transition by international crisis such as the Russian invasion of Ukraine that keep subsidies on fuel consumption. And make European countries divesting from this transition to invest in military budgets.

While the book manages a proper introduction to renewable energy and stays up-to-date with the current developments, I find it a bit overly optimistic on the prospect of achieving COP goals and carbon neutrality. Beyond the geostrategic issues briefly mentioned in the concluding chapter, there is no mention made of the exploding energy consumption of AIs and of the limited investments of AI companies into renewable energies… Reducing energy demand does not even occupy one page of the book (p127). Similarly, I find too little discussion of the political and human aspects of using renewables, eg photovoltaïcs and batteries, which resurfaced in the recent Chinese blockade on rare earths or coverages (as in Nature, 04 Nov 2025) on the extreme hardship of extracting minerals. Contrary to those (aspects) for massive dams affecting the local populations and in the dispute between countries or States. And also little on the environmental costs of producing and recycling both solar and wind farms, in contrast with hydroelectricity. Surprisingly, nuclear energy is evacuated in one paragraph in Chapter 2, on safety arguments. If reappearing in Chapter 6 with further concerns about the overall cost of nuclear energy.

[The usual disclaimer applies, namely that this bicephalic review is likely to appear later in CHANCE, in my book reviews column.]

A modern introduction to probability and statistics [book review]

Posted in Books, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , on July 12, 2025 by xi'an

In the plane to Bengaluru, I read through the book A modern introduction to probability and statistics, by Graham Upton—whose Measuring Animal Abundance I reviewed for CHANCE a while ago—, which is based on the earlier Understanding Statistics, written jointly with Ian Cook. (Not to be confused with A modern introduction to probability and statistics by Dekking et al.) The subtitle is understanding statistical principles in the computer age. Sorry, in the age of the computer. While the cover is most pleasant (and modern), as noticed by an AF flight attendant, the contents are very very standard and could have been written decades ago since the main concession to “the” computer age is the inclusion of a few R commands at the end of most chapters. There are even a few distribution tables here and there (in case “the” computer is not available). But there is no other connection with computational statistics or statistical computing.

The classicism of the contents and the intended audience mean there is little therein on which to either object or criticise. The mixture of elementary probability and basic statistics in a single textbook always feels awkward to me and I think I would have trouble teaching solely from this material. Apart from the glaring typo on the variance of the sum of two correlated random variables on page 87, missing the factor 2 in front of the covariance, while correct(ed) p97 (and the inevitable “the the” typo spotted once). My main criticisms are on the potential confusion between samples and populations in the early chapters, when some statistics are used as motivational examples, as for instance in a (hidden) Monte Carlo stabilisation to the limiting values (p57), way before the Law of Large Numbers is introduced,, the variable mileage in mathematical rigour (while being uncertain that first year students can handle integrals and derivatives), the textbook examples, and the amount of the book contents spent on descriptive statistics and even more on the “classical” tests, with no critical perspective on using point nulls or p-values. The book concludes with a four page (benevolent) chapter on Bayesian statistics that is superfluous imho, or even counterproductive since my experience with a rushed introduction to Bayesian principles almost always result in a rejection of said principles. Plus, the illustration with the coin tossing is not particularly helpful since Andrew maintains that one can load a die, but cannot bias a coin. (A similar reservation on the half-page 289 coverage on pseudo-random generation and Monte Carlo principles for computing p-values.)

Minor (mostly idiosyncratic) remarks follow: CLT prior to LLN,   n-1 in sample sd, little to no model criticism (ntbcf goodness of fit), missing an opportunity when mentioning the varying probability of a day being a birthday (p31) in contrast with BDA cover story, and another opportunity to cite the 2024 Ig Nobel Prize for coin tossing around the LLN, an unclear definition for random variables( p53) and a potentially confusing introduction of Poisson distributions through a informal reference to Poisson processes (and no reason why the years of accession of the kings of Sussex and England till Guillaume—making a return on p178 with the Domesday Book—in 1066 should follow such a process as suggested in Figure 3.5), a surprising definition of the constant e as the special case of exp(x) when x=1 and its series expansion (p70), omitting proofs on laws of sums of iid rv’s by introducing moment generating functions rather late, another obscure reference to a 16th German treatise on surveying as a precursor of the CLT (p131), a proof for the normalising constant of the Normal density that will most likely escape most first year students, a introduction of the t, F, and χ² distributions with no mention of their respective densities (pp141-147), never defining a joint Normal distribution density, insisting on unbiasedness without noting that maximum likelihood—with a strange motivation that it “makes the next sample of n observations most likely to resemble the data in the current sample (p228)—estimators are almost always biased, an abundance of footnotes that may prove of little interest for the youngest readers.

[Disclaimer about potential self-plagiarism as usual: this post or an edited version will eventually appear in my Books Review section in CHANCE.]

Data science ethics [book review]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on May 5, 2025 by xi'an

Data science ethics (concepts, techniques and cautionary tales), by David Martens, was published in 2022 by Oxford University Press. The book is inspired by the author’s  course on Data Science and ethics he has been teaching at the University of Antwerp. (With a link to his slides.) The 255p book proceeds by decomposing the ethics of data science into its different steps: data gathering (Chap. 2), data preprocessing (Chap. 3), modelling (Chap. 4), evaluation (Chap. 5), and deployment (Chap. 6). Following the `FAT Flow Framework´, where FAT stands for fairness, accountability, and transparency.

Do not expect much maths, stats, or anything quantitative: this book is mostly about concepts, even though some (mostly well-known) illustrations are provided. Chapter 2 includes a description of encryption (with homomorphic encryption treated in Chapter 4). And somewhat improbably quantum computing. Differential privacy gets a few pages with not a single formula (until Chapter 4, again).

Chapter 3 covers k-anonymity, record linkage, reidentification, (through cautionary tales) and discrimination through biases in the (learning) dataset.  Chapter 4 is defining ε differential privacy with the Laplace randomization as a possible implementation and with no critical stance on the limitations of the concept. The computation limitations of homomorphic encryption are more clearly pointed out. Federated learning is only quickly mentioned. The section about measuring fairness and reducing bias implies that some prior knowledge is available about whom is potentially discriminated and which covariates to add to the model. The last section on explicability of predictions is worthwhile in signalling the difficulty with most (black box) AI but the example opposing an SVM model to a logistic model is not tremendously convincing in that neither model is true.

Chapter 5 addresses the crucial challenge of ethical evaluation in a rather verbose and vague manner. For instance, with no instruction on how to resist adversarial attacks. Or criticising p-hacking and multiple testing while missing the elephant in the room (p-values!). Drifting from the topic when discussing the misdeeds of Diederik Stapel. Most of the same goes about Chapter 6 and its take on ethical deployment, when going through examples such as Google’s policies in China. Or general musing on the impact of AI on societal inequalities (with mentions of companies and CEOs who have since then back-pedalled on their ethical engagement). These chapters are lacking in tools and (more) practical recommendations.

One interesting aspect of the book is the attention paid to the EU(ropean) aspect of these concerns, through the GDPR (General DAta Protection Regulations) adopted by the European Parliament in 2016. (There is also a brief mention of China’s regulations, but no details beyond a reference. Maybe the Chinese edition differs.)

Arnak Dalalyan at the RSS Journal Webinar

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on October 15, 2023 by xi'an

My friend and CREST colleague Arnak Dalalyan will (re)present [online] a Read Paper at the RSS on 31 October with my friends Hani Doss and Alain Durmus as discussants:

‘Theoretical Guarantees for Approximate Sampling and Log-Concave Densities’

Arnak Dalalyan ENSAE Paris, France

Sampling from various kinds of distributions is an issue of paramount importance in statistics since it is often the key ingredient for constructing estimators, test procedures or confidence intervals. In many situations, exact sampling from a given distribution is impossible or computationally expensive and, therefore, one needs to resort to approximate sampling strategies. However, there is no well-developed theory providing meaningful non-asymptotic guarantees for the approximate sampling procedures, especially in high dimensional problems. The paper makes some progress in this direction by considering the problem of sampling from a distribution having a smooth and log-concave density defined on ℝᵖ⁠, for some integer p > 0. We establish non-asymptotic bounds for the error of approximating the target distribution by the distribution obtained by the Langevin Monte Carlo method and its variants. We illustrate the effectiveness of the established guarantees with various experiments. Underlying our analysis are insights from the theory of continuous time diffusion processes, which may be of interest beyond the framework of log-concave densities that are considered in the present work.

statistical modeling with R [book review]

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on June 10, 2023 by xi'an

Statistical 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.