Archive for Harold Jeffreys

estimation versus testing [again!]

Posted in Books, Statistics, University life with tags , , , , , , , , , , on March 30, 2017 by xi'an

The following text is a review I wrote of the paper “Parameter estimation and Bayes factors”, written by J. Rouder, J. Haff, and J. Vandekerckhove. (As the journal to which it is submitted gave me the option to sign my review.)

The opposition between estimation and testing as a matter of prior modelling rather than inferential goals is quite unusual in the Bayesian literature. In particular, if one follows Bayesian decision theory as in Berger (1985) there is no such opposition, but rather the use of different loss functions for different inference purposes, while the Bayesian model remains single and unitarian.

Following Jeffreys (1939), it sounds more congenial to the Bayesian spirit to return the posterior probability of an hypothesis H⁰ as an answer to the question whether this hypothesis holds or does not hold. This however proves impossible when the “null” hypothesis H⁰ has prior mass equal to zero (or is not measurable under the prior). In such a case the mathematical answer is a probability of zero, which may not satisfy the experimenter who asked the question. More fundamentally, the said prior proves inadequate to answer the question and hence to incorporate the information contained in this very question. This is how Jeffreys (1939) justifies the move from the original (and deficient) prior to one that puts some weight on the null (hypothesis) space. It is often argued that the move is unnatural and that the null space does not make sense, but this only applies when believing very strongly in the model itself. When considering the issue from a modelling perspective, accepting the null H⁰ means using a new model to represent the model and hence testing becomes a model choice problem, namely whether or not one should use a complex or simplified model to represent the generation of the data. This is somehow the “unification” advanced in the current paper, albeit it does appear originally in Jeffreys (1939) [and then numerous others] rather than the relatively recent Mitchell & Beauchamp (1988). Who may have launched the spike & slab denomination.

I have trouble with the analogy drawn in the paper between the spike & slab estimate and the Stein effect. While the posterior mean derived from the spike & slab posterior is indeed a quantity drawn towards zero by the Dirac mass at zero, it is rarely the point in using a spike & slab prior, since this point estimate does not lead to a conclusion about the hypothesis: for one thing it is never exactly zero (if zero corresponds to the null). For another thing, the construction of the spike & slab prior is both artificial and dependent on the weights given to the spike and to the slab, respectively, to borrow expressions from the paper. This approach thus leads to model averaging rather than hypothesis testing or model choice and therefore fails to answer the (possibly absurd) question as to which model to choose. Or refuse to choose. But there are cases when a decision must be made, like continuing a clinical trial or putting a new product on the market. Or not.

In conclusion, the paper surprisingly bypasses the decision-making aspect of testing and hence ends up with a inconclusive setting, staying midstream between Bayes factors and credible intervals. And failing to provide a tool for decision making. The paper also fails to acknowledge the strong dependence of the Bayes factor on the tail behaviour of the prior(s), which cannot be [completely] corrected by a finite sample, hence its relativity and the unreasonableness of a fixed scale like Jeffreys’ (1939).

X-Outline of a Theory of Statistical Estimation

Posted in Books, Statistics, University life with tags , , , , , , , , , , on March 23, 2017 by xi'an

While visiting Warwick last week, Jean-Michel Marin pointed out and forwarded me this remarkable paper of Jerzy Neyman, published in 1937, and presented to the Royal Society by Harold Jeffreys.

“Leaving apart on one side the practical difficulty of achieving randomness and the meaning of this word when applied to actual experiments…”

“It may be useful to point out that although we are frequently witnessing controversies in which authors try to defend one or another system of the theory of probability as the only legitimate, I am of the opinion that several such theories may be and actually are legitimate, in spite of their occasionally contradicting one another. Each of these theories is based on some system of postulates, and so long as the postulates forming one particular system do not contradict each other and are sufficient to construct a theory, this is as legitimate as any other. “

This paper is fairly long in part because Neyman starts by setting Kolmogorov’s axioms of probability. This is of historical interest but also needed for Neyman to oppose his notion of probability to Jeffreys’ (which is the same from a formal perspective, I believe!). He actually spends a fair chunk on explaining why constants cannot have anything but trivial probability measures. Getting ready to state that an a priori distribution has no meaning (p.343) and that in the rare cases it does it is mostly unknown. While reading the paper, I thought that the distinction was more in terms of frequentist or conditional properties of the estimators, Neyman’s arguments paving the way to his definition of a confidence interval. Assuming repeatability of the experiment under the same conditions and therefore same parameter value (p.344).

“The advantage of the unbiassed [sic] estimates and the justification of their use lies in the fact that in cases frequently met the probability of their differing very much from the estimated parameters is small.”

“…the maximum likelihood estimates appear to be what could be called the best “almost unbiassed [sic]” estimates.”

It is also quite interesting to read that the principle for insisting on unbiasedness is one of producing small errors, because this is not that often the case, as shown by the complete class theorems of Wald (ten years later). And that maximum likelihood is somewhat relegated to a secondary rank, almost unbiased being understood as consistent. A most amusing part of the paper is when Neyman inverts the credible set into a confidence set, that is, turning what is random in a constant and vice-versa. With a justification that the credible interval has zero or one coverage, while the confidence interval has a long-run validity of returning the correct rate of success. What is equally amusing is that the boundaries of a credible interval turn into functions of the sample, hence could be evaluated on a frequentist basis, as done later by Dennis Lindley and others like Welch and Peers, but that Neyman fails to see this and turn the bounds into hard values. For a given sample.

“This, however, is not always the case, and in general there are two or more systems of confidence intervals possible corresponding to the same confidence coefficient α, such that for certain sample points, E’, the intervals in one system are shorter than those in the other, while for some other sample points, E”, the reverse is true.”

The resulting construction of a confidence interval is then awfully convoluted when compared with the derivation of an HPD region, going through regions of acceptance that are the dual of a confidence interval (in the sampling space), while apparently [from my hasty read] missing a rule to order them. And rejecting the notion of a confidence interval being possibly empty, which, while being of practical interest, clashes with its frequentist backup.

a response by Ly, Verhagen, and Wagenmakers

Posted in Statistics with tags , , , , , , , , on March 9, 2017 by xi'an

Following my demise [of the Bayes factor], Alexander Ly, Josine Verhagen, and Eric-Jan Wagenmakers wrote a very detailed response. Which I just saw the other day while in Banff. (If not in Schiphol, which would have been more appropriate!)

“In this rejoinder we argue that Robert’s (2016) alternative view on testing has more in common with Jeffreys’s Bayes factor than he suggests, as they share the same ‘‘shortcomings’’.”

Rather unsurprisingly (!), the authors agree with my position on the dangers to ignore decisional aspects when using the Bayes factor. A point of dissension is the resolution of the Jeffreys[-Lindley-Bartlett] paradox. One consequence derived by Alexander and co-authors is that priors should change between testing and estimating. Because the parameters have a different meaning under the null and under the alternative, a point I agree with in that these parameters are indexed by the model [index!]. But with which I disagree when arguing that the same parameter (e.g., a mean under model M¹) should have two priors when moving from testing to estimation. To state that the priors within the marginal likelihoods “are not designed to yield posteriors that are good for estimation” (p.45) amounts to wishful thinking. I also do not find a strong justification within the paper or the response about choosing an improper prior on the nuisance parameter, e.g. σ, with the same constant. Another a posteriori validation in my opinion. However, I agree with the conclusion that the Jeffreys paradox prohibits the use of an improper prior on the parameter being tested (or of the test itself). A second point made by the authors is that Jeffreys’ Bayes factor is information consistent, which is correct but does not solved my quandary with the lack of precise calibration of the object, namely that alternatives abound in a non-informative situation.

“…the work by Kamary et al. (2014) impressively introduces an alternative view on testing, an algorithmic resolution, and a theoretical justification.”

The second part of the comments is highly supportive of our mixture approach and I obviously appreciate very much this support! Especially if we ever manage to turn the paper into a discussion paper! The authors also draw a connection with Harold Jeffreys’ distinction between testing and estimation, based upon Laplace’s succession rule. Unbearably slow succession law. Which is well-taken if somewhat specious since this is a testing framework where a single observation can send the Bayes factor to zero or +∞. (I further enjoyed the connection of the Poisson-versus-Negative Binomial test with Jeffreys’ call for common parameters. And the supportive comments on our recent mixture reparameterisation paper with Kaniav Kamari and Kate Lee.) The other point that the Bayes factor is more sensitive to the choice of the prior (beware the tails!) can be viewed as a plus for mixture estimation, as acknowledged there. (The final paragraph about the faster convergence of the weight α is not strongly

le bayésianisme aujourd’hui [book review]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , , on March 4, 2017 by xi'an

It is quite rare to see a book published in French about Bayesian statistics and even rarer to find one that connects philosophy of science, foundations of probability, statistics, and applications in neurosciences and artificial intelligence. Le bayésianisme aujourd’hui (Bayesianism today) was edited by Isabelle Drouet, a Reader in Philosophy at La Sorbonne. And includes a chapter of mine on the basics of Bayesian inference (à la Bayesian Choice), written in French like the rest of the book.

The title of the book is rather surprising (to me) as I had never heard the term Bayesianism mentioned before. As shown by this link, the term apparently exists. (Even though I dislike the sound of it!) The notion is one of a probabilistic structure of knowledge and learning, à la Poincaré. As described in the beginning of the book. But I fear the arguments minimising the subjectivity of the Bayesian approach should not be advanced, following my new stance on the relativity of probabilistic statements, if only because they are defensive and open the path all too easily to counterarguments. Similarly, the argument according to which the “Big Data” era makesp the impact of the prior negligible and paradoxically justifies the use of Bayesian methods is limited to the case of little Big Data, i.e., when the observations are more or less iid with a limited number of parameters. Not when the number of parameters explodes. Another set of arguments that I find both more modern and compelling [for being modern is not necessarily a plus!] is the ease with which the Bayesian framework allows for integrative and cooperative learning. Along with its ultimate modularity, since each component of the learning mechanism can be extracted and replaced with an alternative. Continue reading

sleeping beauty

Posted in Books, Kids, Statistics with tags , , , , , , , , , on December 24, 2016 by xi'an

Through X validated, W. Huber made me aware of this probability paradox [or para-paradox] of which I had never heard before. One of many guises of this paradox goes as follows:

Shahrazad is put to sleep on Sunday night. Depending on the hidden toss of a fair coin, she is awaken either once (Heads) or twice (Tails). After each awakening, she gets back to sleep and forget that awakening. When awakened, what should her probability of Heads be?

My first reaction is to argue that Shahrazad does not gain information between the time she goes to sleep when the coin is fair and the time(s) she is awaken, apart from being awaken, since she does not know how many times she has been awaken, so the probability of Heads remains ½. However, when thinking more about it on my bike ride to work, I thought of the problem as a decision theory or betting problem, which makes ⅓ the optimal answer.

I then read [if not the huge literature] a rather extensive analysis of the paradox by Ciweski, Kadane, Schervish, Seidenfeld, and Stern (CKS³), which concludes at roughly the same thing, namely that, when Monday is completely exchangeable with Tuesday, meaning that no event can bring any indication to Shahrazad of which day it is, the posterior probability of Heads does not change (Corollary 1) but that a fair betting strategy is p=1/3, with the somewhat confusing remark by CKS³ that this may differ from her credence. But then what is the point of the experiment? Or what is the meaning of credence? If Shahrazad is asked for an answer, there must be a utility or a penalty involved otherwise she could as well reply with a probability of p=-3.14 or p=10.56… This makes for another ill-defined aspect of the “paradox”.

Another remark about this ill-posed nature of the experiment is that, when imagining running an ABC experiment, I could only come with one where the fair coin is thrown (Heads or Tails) and a day (Monday or Tuesday) is chosen at random. Then every proposal (Heads or Tails) is accepted as an awakening, hence the posterior on Heads is the uniform prior. The same would not occurs if we consider the pair of awakenings under Tails as two occurrences of (p,E), but this does not sound (as) correct since Shahrazad only knows of one E: to paraphrase Jeffreys, this is an unobservable result that may have not occurred. (Or in other words, Bayesian learning is not possible on Groundhog Day!)

covariant priors, Jeffreys and paradoxes

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on February 9, 2016 by xi'an

“If no information is available, π(α|M) must not deliver information about α.”

In a recent arXival apparently submitted to Bayesian Analysis, Giovanni Mana and Carlo Palmisano discuss of the choice of priors in metrology. Which reminded me of this meeting I attended at the Bureau des Poids et Mesures in Sèvres where similar debates took place, albeit being led by ferocious anti-Bayesians! Their reference prior appears to be the Jeffreys prior, because of its reparameterisation invariance.

“The relevance of the Jeffreys rule in metrology and in expressing uncertainties in measurements resides in the metric invariance.”

This, along with a second order approximation to the Kullback-Leibler divergence, is indeed one reason for advocating the use of a Jeffreys prior. I at first found it surprising that the (usually improper) prior is used in a marginal likelihood, as it cannot be normalised. A source of much debate [and of our alternative proposal].

“To make a meaningful posterior distribution and uncertainty assessment, the prior density must be covariant; that is, the prior distributions of different parameterizations must be obtained by transformations of variables. Furthermore, it is necessary that the prior densities are proper.”

The above quote is quite interesting both in that the notion of covariant is used rather than invariant or equivariant. And in that properness is indicated as a requirement. (Even more surprising is the noun associated with covariant, since it clashes with the usual notion of covariance!) They conclude that the marginal associated with an improper prior is null because the normalising constant of the prior is infinite.

“…the posterior probability of a selected model must not be null; therefore, improper priors are not allowed.”

Maybe not so surprisingly given this stance on improper priors, the authors cover a collection of “paradoxes” in their final and longest section: most of which makes little sense to me. First, they point out that the reference priors of Berger, Bernardo and Sun (2015) are not invariant, but this should not come as a surprise given that they focus on parameters of interest versus nuisance parameters. The second issue pointed out by the authors is that under Jeffreys’ prior, the posterior distribution of a given normal mean for n observations is a t with n degrees of freedom while it is a t with n-1 degrees of freedom from a frequentist perspective. This is not such a paradox since both distributions work in different spaces. Further, unless I am confused, this is one of the marginalisation paradoxes, which more straightforward explanation is that marginalisation is not meaningful for improper priors. A third paradox relates to a contingency table with a large number of cells, in that the posterior mean of a cell probability goes as the number of cells goes to infinity. (In this case, Jeffreys’ prior is proper.) Again not much of a bummer, there is simply not enough information in the data when faced with a infinite number of parameters. Paradox #4 is the Stein paradox, when estimating the squared norm of a normal mean. Jeffreys’ prior then leads to a constant bias that increases with the dimension of the vector. Definitely a bad point for Jeffreys’ prior, except that there is no Bayes estimator in such a case, the Bayes risk being infinite. Using a renormalised loss function solves the issue, rather than introducing as in the paper uniform priors on intervals, which require hyperpriors without being particularly compelling. The fifth paradox is the Neyman-Scott problem, with again the Jeffreys prior the culprit since the estimator of the variance is inconsistent. By a multiplicative factor of 2. Another stone in Jeffreys’ garden [of forking paths!]. The authors consider that the prior gives zero weight to any interval not containing zero, as if it was a proper probability distribution. And “solve” the problem by avoid zero altogether, which requires of course to specify a lower bound on the variance. And then introducing another (improper) Jeffreys prior on that bound… The last and final paradox mentioned in this paper is one of the marginalisation paradoxes, with a bizarre explanation that since the mean and variance μ and σ are not independent a posteriori, “the information delivered by x̄ should not be neglected”.

on the origin of the Bayes factor

Posted in Books, Statistics with tags , , , , , , , on November 27, 2015 by xi'an

Alexander Etz and Eric-Jan Wagenmakers from the Department of Psychology of the University of Amsterdam just arXived a paper on the invention of the Bayes factor. In particular, they highlight the role of John Burdon Sanderson (J.B.S.) Haldane in the use of the central tool for Bayesian comparison of hypotheses. In short, Haldane used a Bayes factor before Jeffreys did!

“The idea of a significance test, I suppose, putting half the probability into a constant being 0, and distributing the other half over a range of possible values.”H. Jeffreys

The authors analyse Jeffreys’ 1935 paper on significance tests, which appears to be the very first occurrence of a Bayes factor in his bibliography, testing whether or not two probabilities are equal. They also show the roots of this derivation in earlier papers by Dorothy Wrinch and Harold Jeffreys. [As an “aside”, the early contributions of Dorothy Wrinch to the foundations of 20th Century Bayesian statistics are hardly acknowledged. A shame, when considering they constitute the basis and more of Jeffreys’ 1931 Scientific Inference, Jeffreys who wrote in her necrology “I should like to put on record my appreciation of the substantial contribution she made to [our joint] work, which is the basis of all my later work on scientific inference.” In retrospect, Dorothy Wrinch should have been co-author to this book…] As early as 1919. These early papers by Wrinch and Jeffreys are foundational in that they elaborate a construction of prior distributions that will eventually see the Jeffreys non-informative prior as its final solution [Jeffreys priors that should be called Lhostes priors according to Steve Fienberg, although I think Ernest Lhoste only considered a limited number of transformations in his invariance rule]. The 1921 paper contains de facto the Bayes factor but it does not appear to be advocated as a tool per se for conducting significance tests.

“The historical records suggest that Haldane calculated the first Bayes factor, perhaps almost by accident, before Jeffreys did.” A. Etz and E.J. Wagenmakers

As another interesting aside, the historical account points out that Jeffreys came out in 1931 with what is now called Haldane’s prior for a Binomial proportion, proposed in 1931 (when the paper was read) and in 1932 (when the paper was published in the Mathematical Proceedings of the Cambridge Philosophical Society) by Haldane. The problem tackled by Haldane is again a significance on a Binomial probability. Contrary to the authors, I find the original (quoted) text quite clear, with a prior split before a uniform on [0,½] and a point mass at ½. Haldane uses a posterior odd [of 34.7] to compare both hypotheses but… I see no trace in the quoted material that he ends up using the Bayes factor as such, that is as his decision rule. (I acknowledge decision rule is anachronistic in this setting.) On the side, Haldane also implements model averaging. Hence my reading of this reading of the 1930’s literature is that it remains unclear that Haldane perceived the Bayes factor as a Bayesian [another anachronism] inference tool, upon which [and only which] significance tests could be conducted. That Haldane had a remarkably modern view of splitting the prior according to two orthogonal measures and of correctly deriving the posterior odds is quite clear. With the very neat trick of removing the infinite integral at p=0, an issue that Jeffreys was fighting with at the same time. In conclusion, I would thus rephrase the major finding of this paper as Haldane should get the priority in deriving the Bayesian significance test for point null hypotheses, rather than in deriving the Bayes factor. But this may be my biased views of Bayes factors speaking there…

Another amazing fact I gathered from the historical work of Etz and Wagenmakers is that Haldane and Jeffreys were geographically very close while working on the same problem and hence should have known and referenced their respective works. Which did not happen.