Mýrin (“The Bog”) is the third novel in the Inspector Erlendur series written by Arnaldur Indridason. It contains the major themes of the series, from the fascination for unexplained disappearances in Iceland to Elendur’s inability to deal with his family responsibilities, to domestic violence, to exhumations. The death that starts the novel takes place in the district of Norðurmýri, “the northern marsh”, not far from the iconic Hallgrimskirkja, and not far either from DeCODE, the genetic company I visited last June and which stores genetic information about close to a million Icelanders, the Íslendingabók. And which plays an important and nefarious role in the current novel. While this episode takes place mostly between Reykjavik and Keflavik, hence does not offer any foray into Icelandic landscapes, it reflects quite vividly on the cultural pressure still present in the recent years to keep rapes and sexual violence a private matter, hidden from an indifferent or worse police force. It also shows how the police misses (in 2001) the important genetic clues for being yet unaware of the immense and frightening possibilities of handling the genetic code of an entire population. (The English and French titles refer to the unauthorised private collections of body part accumulated [in jars] by doctors after autopsies, families being unaware of the fact.) As usual, solving the case is the least important part of the story, which tells about broken lifes and survivors against all odds.
Archive for book review
“The crux of the situation is that we lack theoretical insight into even quite basic questions about what is going on. More particularly, we cannot sayy anything about the limiting posterior marginal distribution of α compared to the prior marginal distribution of α.” (p.142)
Bayesian inference for partially identified models is a recent CRC Press book by Paul Gustafson that I received for a review in CHANCE with keen interest! If only because the concept of unidentifiability has always puzzled me. And that I have never fully understood what I felt was a sort of joker card that a Bayesian model was the easy solution to the problem since the prior was compensating for the components of the parameter not identified by the data. As defended by Dennis Lindley that “unidentifiability causes no real difficulties in the Bayesian approach”. However, after reading the book, I am less excited in that I do not feel it answers this type of questions about non-identifiable models and that it is exclusively centred on the [undoubtedly long-term and multifaceted] research of the author on the topic.
“Without Bayes, the feeling is that all the data can do is locate the identification region, without conveying any sense that some values in the region are more plausible than others.” (p.47)
Overall, the book is pleasant to read, with a light and witty style. The notational conventions are somewhat unconventional but well explained, to distinguish θ from θ* from θ†. The format of the chapters is quite similar with a definition of the partially identified model, an exhibition of the transparent reparameterisation, the computation of the limiting posterior distribution [of the non-identified part], a demonstration [which it took me several iterations as the English exhibition rather than the French proof, pardon my French!]. Chapter titles suffer from an excess of the “further” denomination… The models themselves are mostly of one kind, namely binary observables and non-observables leading to partially observed multinomials with some non-identifiable probabilities. As in missing-at-random models (Chapter 3). In my opinion, it is only in the final chapters that the important questions are spelled-out, not always faced with a definitive answer. In essence, I did not get from the book (i) a characterisation of the non-identifiable parts of a model, of the identifiability of unidentifiability, and of the universality of the transparent reparameterisation, (ii) a tool to assess the impact of a particular prior and possibly to set it aside, and (iii) a limitation to the amount of unidentifiability still allowing for coherent inference. Hence, when closing the book, I still remain in the dark (or at least in the grey) on how to handle partially identified models. The author convincingly argues that there is no special advantage to using a misspecified if identifiable model to a partially identified model, for this imbues false confidence (p.162), however we also need the toolbox to verify this is indeed the case.
“Given the data we can turn the Bayesian computational crank nonetheless and see what comes out.” (p.xix)
“It is this author’s contention that computation with partially identified models is a “bottleneck” issue.” (p.141)
Bayesian inference for partially identified models is particularly concerned about computational issues and rightly so. It is however unclear to me (without more time to invest investigating the topic) why the “use of general-purpose software is limited to the [original] parametrisation” (p.24) and why importance sampling would do better than MCMC on a general basis. I would definitely have liked more details on this aspect. There is a computational considerations section at the end of the book, but it remains too allusive for my taste. My naïve intuition would be that the lack of identifiability leads to flatter posterior and hence to easier MCMC moves, but Paul Gustafson reports instead bad mixing from standard MCMC schemes (like WinBUGS).
In conclusion, the book opens a new perspective on the relevance of partially identifiable models, trying to lift the stigma associated with them, and calls for further theory and methodology to deal with those. Here are the author’s final points (p.162):
- “Identification is nuanced. Its absence does not preclude a parameter being well estimated, not its presence guarantee a parameter can be well estimated.”
- “If we really took limitations of study designs and data quality seriously, then partially identifiable models would crop up all the time in a variety of scientific fields.”
- “Making modeling assumptions for the sole purpose of gaining full identification can be a mug’s game (…)”
- “If we accept partial identifiability, then consequently we need to regard sample size differently. There are profound implications of posterior variance tending to a positive limit as the sample size grows.”
These points may be challenging enough to undertake to read Bayesian inference for partially identified models in order to make one’s mind about their eventual relevance in statistical modelling.
[Disclaimer about potential self-plagiarism: this post will also be published as a book review in my CHANCE column. ]
When visiting a bookstore in Florence last month, during our short trip to Tuscany, I came upon this book with enough of a funny cover and enough of a funny title (possibly capitalising on the similarity with “the girl who played with fire”] to make me buy it. I am glad I gave in to this impulse as the book is simply hilarious! The style and narrative relate rather strongly to the series of similarly [mostly] hilarious picaresque tales written by Paasilina and not only because both authors are from Scandinavia. There is the same absurd feeling that the book characters should not have this sort of things happening to them and still the morbid fascination to watch catastrophe after catastrophe being piled upon them. While the story is deeply embedded within the recent history of South Africa and [not so much] of Sweden for the past 30 years, including major political figures, there is no true attempt at making the story in the least realistic, which is another characteristic of the best stories of Paasilina. Here, a young girl escapes the poverty of the slums of Soweto, to eventually make her way to Sweden along with a spare nuclear bomb and a fistful of diamonds. Which alas are not eternal… Her intelligence helps her to overcome most difficulties, but even her needs from time to time to face absurd situations as another victim. All is well that ends well for most characters in the story, some of whom one would prefer to vanish in a gruesome accident. Which seemed to happen until another thread in the story saved the idiot. The satire of South Africa and of Sweden is most enjoyable if somewhat easy! Now I have to read the previous volume in the series, The Hundred-Year-Old Man Who Climbed Out of the Window and Disappeared!
When in Warwick last October, I met Simo Särkkä, who told me he had published an IMS monograph on Bayesian filtering and smoothing the year before. I thought it would be an appropriate book to review for CHANCE and tried to get a copy from Oxford University Press, unsuccessfully. I thus bought my own book that I received two weeks ago and took the opportunity of my Czech vacations to read it… [A warning pre-empting accusations of self-plagiarism: this is a preliminary draft for a review to appear in CHANCE under my true name!]
“From the Bayesian estimation point of view both the states and the static parameters are unknown (random) parameters of the system.” (p.20)
Bayesian filtering and smoothing is an introduction to the topic that essentially starts from ground zero. Chapter 1 motivates the use of filtering and smoothing through examples and highlights the naturally Bayesian approach to the problem(s). Two graphs illustrate the difference between filtering and smoothing by plotting for the same series of observations the successive confidence bands. The performances are obviously poorer with filtering but the fact that those intervals are point-wise rather than joint, i.e., that the graphs do not provide a confidence band. (The exercise section of that chapter is superfluous in that it suggests re-reading Kalman’s original paper and rephrases the Monty Hall paradox in a story unconnected with filtering!) Chapter 2 gives an introduction to Bayesian statistics in general, with a few pages on Bayesian computational methods. A first remark is that the above quote is both correct and mildly confusing in that the parameters can be consistently estimated, while the latent states cannot. A second remark is that justifying the MAP as associated with the 0-1 loss is incorrect in continuous settings. The third chapter deals with the batch updating of the posterior distribution, i.e., that the posterior at time t is the prior at time t+1. With applications to state-space systems including the Kalman filter. The fourth to sixth chapters concentrate on this Kalman filter and its extension, and I find it somewhat unsatisfactory in that the collection of such filters is overwhelming for a neophyte. And no assessment of the estimation error when the model is misspecified appears at this stage. And, as usual, I find the unscented Kalman filter hard to fathom! The same feeling applies to the smoothing chapters, from Chapter 8 to Chapter 10. Which mimic the earlier ones. Continue reading
I read this book by Albert Camus over my week in Oxford, having found it on my daughter’s bookshelf (as she had presumably read it in high school…). It is a very special book in that (a) Camus was working on it when he died in a car accident, (b) the manuscript was found among the wreckage, and (c) it differs very much from Camus’ other books. Indeed, the book is partly autobiographical and written with an unsentimental realism that is raw and brutal. It describes the youth of Jacques, the son of French colons in Algiers, whose father had died in the first days of WW I and whose family lives in the uttermost poverty, with both his mother and grandmother doing menial jobs to simply survive. Thanks to a supportive teacher, he manages to get a grant to attend secondary school. What is most moving about the book is how Camus describes the numbing effects of poverty, namely how his relatives see their universe shrinking so much that notions like the Mother Country (France) or books loose meaning for them. Without moving them towards or against native Algerians, who never penetrate the inner circles in the novel, moving behind a sort of glass screen. It is not that the tensions and horrors of the colonisation and of the resistance to colonisation are hidden, quite the opposite, but the narrator considers those with a sort of fatalism without questioning the colonisation itself. (The book reminded me very much of my grand-father‘s childhood, with a father also among the dead soldiers of WW I, being raised by a single mother in harsh conditions. With the major difference that my grandfather decided to stop school very early to become a gardener…) There are also obvious parallels with Pagnol’s autobiographical novels like My Father’s Glory, written at about the same time, from the boy friendship to the major role of the instituteur, to the hunting party, to the funny uncle, but everything opposes the two authors, from Pagnol light truculence to Camus’ tragic depiction. Pagnol’s books are great teen books (and I still remember my mother buying the first one on a vacation road trip) but nothing more. Camus’ book could have been his greatest book, had he survived the car accident of January 1960.
Here is the fifth instalment in the Peter Grant (or Rivers of London) series by Ben Aaronovitch. Thus entitled Foxglove summer, which meaning only became clear (to me) by the end of the book. I found it in my mailbox upon arrival in Warwick last Sunday. And rushed through the book during evenings, insomnia breaks and even a few breakfasts!
“It’s observable but not reliably observable. It can have a quantifiable effects, but resists any attempt to apply mathematical principles to it – no wonder Newton kept magic under wraps. It must have driven him mental. Or maybe not.” (p.297)
Either because the author has run out of ideas to centre a fifth novel on a part or aspect of London (even though the parks, including the London Zoo, were not particularly used in the previous novels), or because he could not set this new type of supernatural in a city (no spoilers!), this sequel takes place in the Western Counties, close to the Welsh border (and not so far from Brother Cadfael‘s Shrewbury!). It is also an opportunity to introduce brand new (local) characters which are enjoyable if a wee bit of a caricature! However, the inhabitants of the small village where the kidnapping investigation takes place are almost too sophisticated for Peter Grant who has to handle the enquiry all by himself, as his mentor is immobilised in London by the defection of Peter’s close colleague, Lindsey.
“We trooped off (…) down something that was not so much a path as a statistical variation in the density of the overgrowth.” (p.61)
As usual, the dialogues and monologues of Grant are the most enjoyable part of the story, along with a development of the long-in-the-coming love affair with the river goddess Beverley Brooks. And a much appreciated ambiguity in the attitude of Peter about the runaway Lindsey… The story itself reflects the limitations of a small village where one quickly repeats over and over the same trips and the same relations. Which gives a sensation of slow motion, even in the most exciting moments. The resolution of the enigma is borrowing too heavily to the fae and elves folklore, even though the final pages bring a few surprises. Nonetheless, the whole book was a page-turner for me, meaning I spent more time reading it this week than I intended or than was reasonable. No wonder for a series taking place in The Folly!
A book from the pile I brought back from Gainesville. And the first I read, mostly during the trip back to Paris. Both because I was eager to see the sequel to Rivers of London and because it was short and easy to carry in a pocket.
“From the figures I have, I believe that two to three jazz musicians have died within twenty-four hours of playing a gig in the Greater London area in the last year.”
“I take it that’s statistically significant?“
Moon over Soho is the second installment in the Peter Grant series by Ben Aaronovitch. It would not read well on its own as it takes over when Rivers of London stopped. Even though it reintroduces most of the rules of this magical universe. Most characters are back (except for the hostaged Beverly) and they are trying to cope with what happened in the first installment. The story is even more centred on jazz than in the first volume, with as a corollary, Peter Grant’s parents taking a more important part in the book. The recovering Leslie is hardly seen (for obvious reasons) and heard, which leaves a convenient hole in Grant’s sentimental life! The book also introduces a major magical villein who will undoubtedly figures in the incoming books. Another great story, even though the central plot has a highly predictable ending, and even more end of the ending, and some parts sound like repetitions of similar parts in the first volume. But the tone, the pace, the style, the humour, the luv’ of Lundun, all are there and so it is all that matters! (I again bemoan the missing map of London!)