Haldane’s short autobiography

“I was born at Oxford, England, in 1892.  My father was Prof. J.S. Haldane, the physiologist.  I was educated at Eton and New College, Oxford.  I learned much of my science by apprenticeship, assisting my father from the age of eight onwards, and my university degree is in for classics, not science.  I was in a British infantry battalion from 1914 to 1919, and was twice wounded.  I began scientific research in 1910, and became a Fellow of New College, Oxford, in 1919.  I was at Cambridge from 1922-1932 as Reader in Biochemistry, and have been a professor in London University since 1933.  I was visiting professor in the University of Berkeley, Cal., in 1932.  In the same year I was elected a Fellow of the Royal Society of London.

My scientific work has been varied.  In the field of human physiology I am best known for my work on the effects of taking large amounts of ammonium chloride and other salts.  This has had some application in treating lead and radium poisoning.  In the field of genetics I was the first to discover linkage in mammals, to map a human chromosome, and (with Penrose) to measure the mutation rate of a human gene.  I have also made some minor discoveries in mathematics.

Whilst I may have been a credit to my universities, I have been a trial in other ways.  I was dismissed from Cambridge University in 1926 in connexion with a divorce case, but regained my post on appeal to a higher tribunal, which found that the university authorities had decided to dismiss me without hearing my case.  At present I have refused to evacuate University College, London, and, with two assistants am its sole academic occupant.  I am carrying on research there under difficulties.

Besides strictly scientific books I have written a number of popular works including a book of children’s stories.  I consider that a scientist, if he can do so, should help to render science intelligible to ordinary people, and have done my best to popularize it.

Till 1933 I tried to keep out of politics, but the support given by the British Government to Hitler and Mussolini forced me to enter the political field.  In 1936-1938 I spent three months in Republican Spain, first as an adviser on gas protection, and then as an observer of air raid precautions.  I was in the front line during fighting, and in several air raids behind the line.  Since then I have tried, with complete lack of success, to induce the British Government to adopt air raid protection measures which had proved their efficacy in Spain.

Mr. Chamberlain’s policy, and the recent developments in physics and biology, combined to convince me of the truth of the Marxist philosophy.  Though I am a member of no political party, I have of late years supported the communist party on a number of issues.  At present I am engaged on research in genetics, & research intended to save the lives of members of the British armed forces, and writing and public speaking designed to prevent the spreading of the present war, and if possible to bring about peace.  I am a fairly competent public speaker.

It will be seen that my life has been a full one.  I have been married for 14 years, measure 73 inches, weigh 245 pounds, and enjoy swimming and mountain walking.  I am bald and blue-eyed, a moderate drinker and a heavy smoker. I can read 11 languages and make public speeches in three, but am unmusical.”

J.B.S. Haldane, circa 1940

in the name of eugenics [book review]

In preparation for the JSM round table on eugenics and statistics, organised by the COPSS Award Committee, I read the 1985 book of Daniel Kevles, In the Name of Eugenics: Genetics and the Uses of Human Heredity, as recommended by Stephen Stiegler. While a large part of the book was published in The New Yorker, in which Kevles published on a regular basis, and while he abstains from advanced methodological descriptions, focussing more on the actors of this first attempt at human genetics and of the societal consequences of biased interpretations and mistaken theories, his book is a scholarly accomplishment, with a massive section of notes and numerous references. This is a comparative history of eugenics from the earliest (Francis Galton, 1865) to the current days (1984) since “modern eugenics” survived the exposure of the Nazi crimes (including imposed sterilizations that are still enforced to this day). Comparative between the UK and the US, however, hardly considering other countries, except for a few connections with Germany and the Soviet Union, albeit in the sole perspective of Muller’s sojourn there and the uneasy “open-minded” approach to Lysenkoism by Haldane. (Japan is also mentioned in connection with Neel’s study of the genetic impact of the atomic bombs.) While discussing the broader picture, the book mostly concentrates on the scientific aspects, on how the misguided attempts to reduce intelligence to IQ tests or to a single gene, and to improve humanity (or some of its subgroups) by State imposed policies perceived as crude genetic engineering simultaneously led to modern genetics and a refutation of eugenic perspectives by most if not all. There is very little about statistical methodology per, beside stories on the creation of Biometrika and the Annals of Eugenics, but much more on the accumulation of data by eugenic societies and the exploitation of this data for ideological purposes. Galton and Pearson get the lion’s share of the book, while Fisher does not get more coverage than Haldane or Penrose. Overall, I found the book immensely informative as exposing the diversity of scientific and pseudo-scientific viewpoints within eugenism and its evolution towards human genetics as a scientific endeavour.

another book on J.B.S. Haldane [review of a book review]

As I noticed a NYT book review of a most recent book on J.B.S. Haldane, I realised several other books had already been written about him. From an early 1985 biography, “Haldane: the life and work of J.B.S. Haldane with special references to India” followed by a “2016 biography “Popularizing Science” along an  2009 edited book on some Haldane’s essays, “What I require from life“, all by Krishna R. Dronamraju to a 1969 biography with the cryptic title “J.B.S.“, by Richard Clarke, along with a sensational 2018 “Comrade Haldane Is Too Busy to Go on Holiday: The Genius Who Spied for Stalin” by Gavan Tredoux, depicting him as a spy for the Soviet Union during WW II. (The last author is working on a biography of Francis Galton, hopefully exonerating him of spying for the French! But a short text of him comparing Haldane and Darlington appears to support the later’s belief in racial differences in intelligence…) I also discovered that J.B.S. had written a children book, “Mr Friend Mr. Leaky“, illustrated by Quentin Blake, Roald Dahl’s illustrator. (Charlotte Franken Haldane, J.B.S.’s first wife, also wrote a considerable number of books.)

The NYT review is more a summary of Haldane’s life than an analysis of the book itself, hard as it is not to get mesmerised by the larger-than-life stature of J.B.S. It does not dwell very long on the time it took Haldane to break from the Communist Party for its adherence to the pseudo-science Lysenko (while his wife Charlotte had realised the repressive nature of the Soviet regime much earlier, which may have led to their divorce). While the review makes no mention at all of Haldane’s ideological move to the ISI in Kolkata, it concludes with “for all his failings, he was “deeply attractive during a time of shifting, murky moralities.”” [The double quotes being the review quoting the book!]

approximation of improper by vague priors

“…many authors prefer to replace these improper priors by vague priors, i.e. probability measures that aim to represent very few knowledge on the parameter.”

Christèle Bioche and Pierre Druihlet arXived a few days ago a paper with this title. They aim at bringing a new light on the convergence of vague priors to their limit. Their notion of convergence is a pointwise convergence in the quotient space of Radon measures, quotient being defined by the removal of the “normalising” constant. The first results contained in the paper do not show particularly enticing properties of the improper limit of proper measures as the limit cannot be given any (useful) probabilistic interpretation. (A feature already noticeable when reading Jeffreys.) The first result that truly caught my interest in connection with my current research is the fact that the Haar measures appear as a (weak) limit of conjugate priors (Section 2.5). And that the Jeffreys prior is the limit of the parametrisation-free conjugate priors of Druilhet and Pommeret (2012, Bayesian Analysis, a paper I will discuss soon!). The result about the convergence of posterior means is rather anticlimactic as the basis assumption is the uniform integrability of the sequence of the prior densities. An interesting counterexample (somehow familiar to invariance fans): the sequence of Poisson distributions with mean n has no weak limit. And the Haldane prior does appear as a limit of Beta distributions (less surprising). On (0,1) if not on [0,1].

The paper contains a section on the Jeffreys-Lindley paradox, which is only considered from the second perspective, the one I favour. There is however a mention made of the noninformative answer, which is the (meaningless) one associated with the Lebesgue measure of normalising constant one. This Lebesgue measure also appears as a weak limit in the paper, even though the limit of the posterior probabilities is 1. Except when the likelihood has bounded variations outside compacts. Then the  limit of the probabilities is the prior probability of the null… Interesting, truly, but not compelling enough to change my perspective on the topic. (And thanks to the authors for their thanks!)

bioinformatics workshop at Pasteur

Once again, I (did) find myself attending lectures on a Monday! This time, it was at the Institut Pasteur, (where I did not spot any mention of Alexandre Yersin) in the bioinformatics unit, around Bayesian methods in computational biology. The workshop was organised by Michael Nilges and the program started as follows:

9:10 AM Michael Habeck (MPI Göttingen) Bayesian methods for cryo-EM
9:50 AM John Chodera (Sloan-Kettering research institute) Toward Bayesian inference of conformational distributions, analysis of isothermal titration calorimetry experiments, and forcefield parameters
11:00 AM Jeff Hoch (University of Connecticut Health Center) Haldane, Bayes, and Reproducible Research: Bedrock Principles for the Era of Big  Data
11:40 AM Martin Weigt (UPMC Paris) Direct-Coupling Analysis: From residue co-evolution to structure prediction
12:20 PM Riccardo Pellarin (UCSF) Modeling the structure of macromolecules using cross-linking data
2:20 PM Frederic Cazals (INRIA Sophia-Antipolis) Coarse-grain Modeling of Large Macro-Molecular Assemblies: Selected Challenges
3:00 PM Yannick Spill (Institut Pasteur) Bayesian Treatment of SAXS Data
3:30 PM Guillaume Bouvier (Institut Pasteur) Clustering protein conformations using Self-Organizing Maps

This is a highly interesting community, from which stemmed many of the MC and MCMC ideas, but I must admit I got lost (in translation) most of the time (and did not attend the workshop till its end), just like when I attended this workshop at the German synchrotron in Hamburg last Spring: some terms and concepts were familiar like Gibbs sampling, Hamiltonian MCMC, HMM modelling, EM steps, maximum entropy priors, reversible jump MCMC, &tc., but the talks were going too fast (for me) and focussed instead on the bio-chemical aspects, like protein folding, entropy-enthalpy, free energy, &tc. So the following comments mostly reflect my being alien to this community…

For instance, I found the talk by John Chodera quite interesting (in a fast-forward high-energy/content manner), but the probabilistic modelling was mostly absent from his slides (and seemed to reduce to a Gaussian likelihood) and the defence of Bayesian statistics sounded a bit like a mantra at times (something like “put a prior on everything you do not know and everything will end up fine with enough simulations”), a feature I once observed in the past with Bayesian ideas coming to a new field (although this hardly seems to be the case here).

All talks I attended mentioned maximum entropy as a way of modelling, apparently a common tool in this domain (as there were too little details for me). For instance, Jeff Hoch’s talk remained at a very general level, referring to a large literature (incl. David Donoho’s) for the advantages of using MaxEnt deconvolution to preserve sensitivity. (The “Haldane” part of his talk was about Haldane —who moved from UCL to the ISI in Calcutta— writing a parody on how to fake genetic data in a convincing manner. And showing the above picture.) Although he linked them with MaxEnt principles, Martin Weigt’s talk was about Markov random fields modelling contacts between amino acids in the protein, but I could not get how the selection among the huge number of possible models was handled: To me it seemed to amount to estimate a graphical model on the protein, as it also did for my neighbour. (No sign of any ABC processing in the picture.)