day four at ISBA 22

Woke up an hour later today! Which left me time to work on [shortening] my slides for tomorrow, run to Mon(t) Royal, and bike to St-Viateur Bagels for freshly baked bagels. (Which seemed to be missing salt, despite my low tolerance for salt in general.)

Terrific plenary lecture by Pierre Jacob in his Susie Bayarri’s Lecture about cut models!  Offering a very complete picture of the reasons for seeking modularisation, the theoretical and practical difficulties with the approach, and some asymptotics as well. Followed a great discussion by Judith on cut posteriors separating interest parameters from nuisance parameters, especially in semi-parametric models. Even introducing two priors on the same parameters! And by Jim Berger, who coauthored with Susie the major cut paper inspiring this work, and illustrated the concept on computer experiments (not falling into the fallacy pointed out by Martin Plummer at MCMski(v) in Chamonix!).

Speaking of which, the Scientific Committee for the incoming BayesComp²³ in Levi, Finland, had a working meeting to which I participated towards building the programme as it is getting near. For those interested in building a session, they should make preparations and take advantage of being together in Mon(t)réal, as the call is coming out pretty soon!

Attended a session on divide-and-conquer methods for dependent data, with Sanvesh Srivastava considering the case of hidden Markov models and block processing the observed sequence. Which is sort of justified by the forgettability of long-past observations. I wonder if better performances could be achieved otherwise as the data on a given time interval gives essentially information on the hidden chain at other time periods.

I was informed this morn that Jackie Wong, one speaker in our session tomorrow could not make it to Mon(t)réal for visa reasons. Which is unfortunate for him, the audience and everyone involved in the organisation. This reinforces my call for all-time hybrid conferences that avoid penalising (or even discriminating) against participants who cannot physically attend for ethical, political (visa), travel, health, financial, parental, or any other, reasons… I am often opposed the drawbacks of lower attendance, risk of a deficit, dilution of the community, but there are answers to those, existing or to be invented, and the huge audience at ISBA demonstrates a need for “real” meetings that could be made more inclusive by mirror (low-key low-cost) meetings.

Finished the day at Isle de Garde with a Pu Ehr flavoured beer, in a particularly lively (if not jazzy) part of the city…

approximation of Bayes Factors via mixing

A [new version of a] paper by Chenguang Dai and Jun S. Liu got my attention when it appeared on arXiv yesterday. Due to its title which reminded me of a solution to the normalising constant approximation that we proposed in the 2010 nested sampling evaluation paper we wrote with Nicolas. Recovering bridge sampling—mentioned by Dai and Liu as an alternative to their approach rather than an early version—by a type of Charlie Geyer (1990-1994) trick. (The attached slides are taken from my MCMC graduate course, with a section on the approximation of Bayesian normalising constants I first wrote for a short course at Jim Berger’s 70th anniversary conference, in San Antonio.)

A difference with the current paper is that the authors “form a mixture distribution with an adjustable mixing parameter tuned through the Wang-Landau algorithm.” While we chose it by hand to achieve sampling from both components. The weight is updated by a simple (binary) Wang-Landau version, where the partition is determined by which component is simulated, ie by the mixture indicator auxiliary variable. Towards using both components on an even basis (à la Wang-Landau) and stabilising the resulting evaluation of the normalising constant. More generally, the strategy applies to a sequence of surrogate densities, which are chosen by variational approximations in the paper.

the buzz about nuzz

“…expensive in these terms, as for each root, Λ(x(s),v) (at the cost of one epoch) has to be evaluated for each root finding iteration, for each node of the numerical integral“

When using the ZigZag sampler, the main (?) difficulty is in producing velocity switch as the switches are produced as interarrival times of an inhomogeneous Poisson process. When the rate of this process cannot be integrated out in an analytical manner, the only generic approach I know is in using Poisson thinning, obtained by finding an integrable upper bound on this rate, generating from this new process and subsampling. Finding the bound is however far from straightforward and may anyway result in an inefficient sampler. This new paper by Simon Cotter, Thomas House and Filippo Pagani makes several proposals to simplify this simulation, Nuzz standing for numerical ZigZag. Even better (!), their approach is based on what they call the Sellke construction, with Tom Sellke being a probabilist and statistician at Purdue University (trivia: whom I met when spending a postdoctoral year there in 1987-1988) who also wrote a fundamental paper on the opposition between Bayes factors and p-values with Jim Berger.

“We chose as a measure of algorithm performance the largest Kolmogorov-Smirnov (KS) distance between the MCMC sample and true distribution amongst all the marginal distributions.”

The practical trick is rather straightforward in that it sums up as the exponentiation of the inverse cdf method, completed with a numerical resolution of the inversion. Based on the QAGS (Quadrature Adaptive Gauss-Kronrod Singularities) integration routine. In order to save time Kingman’s superposition trick only requires one inversion rather than d, the dimension of the variable of interest. This nuzzled version of ZIgZag can furthermore be interpreted as a PDMP per se. Except that it retains a numerical error, whose impact on convergence is analysed in the paper. In terms of Wasserstein distance between the invariant measures. The paper concludes with a numerical comparison between Nuzz and random walk Metropolis-Hastings, HMC, and manifold MALA, using the number of evaluations of the likelihood as a measure of time requirement. Tuning for Nuzz is described, but not for the competition. Rather dramatically the Nuzz algorithm performs worse than this competition when counting one epoch for each likelihood computation and better when counting one epoch for each integral inversion. Which amounts to perfect inversion, unsurprisingly. As a final remark, all models are more or less Normal, with very smooth level sets, maybe not an ideal range

 

same risk, different estimators

An interesting question on X validated reminded me of the epiphany I had some twenty years ago when reading a Annals of Statistics paper by Anirban Das Gupta and Bill Strawderman on shrinkage estimators, namely that some estimators shared the same risk function, meaning their integrated loss was the same for all values of the parameter. As indicated in this question, Stefan‘s instructor seems to believe that two estimators having the same risk function must be a.s. identical. Which is not true as exemplified by the James-Stein (1960) estimator with scale 2(p-2), which has constant risk p, just like the maximum likelihood estimator. I presume the confusion stemmed from the concept of completeness, where having a function with constant expectation under all values of the parameter implies that this function is constant. But, for loss functions, the concept does not apply since the loss depends both on the observation (that is complete in a Normal model) and on the parameter.

on Dutch book arguments

“Reality is not always probable, or likely.”― Jorge Luis Borges

As I am supposed to discuss Teddy Seidenfeld‘s talk at the Bayes, Fiducial and Frequentist conference in Harvard today [the snow happened last time!], I started last week [while driving to Wales] reading some related papers of his. Which is great as I had never managed to get through the Dutch book arguments, including those in Jim’s book.

The paper by Mark Schervish, Teddy Seidenfeld, and Jay Kadane is defining coherence as the inability to bet against the predictive statements based on the procedure. A definition that sounds like a self-fulfilling prophecy to me as it involves a probability measure over the parameter space. Furthermore, the notion of turning inference, which aims at scientific validation, into a leisure, no-added-value, and somewhat ethically dodgy like gambling, does not agree with my notion of a validation for a theory. That is, not as a compelling reason for adopting a Bayesian approach. Not that I have suddenly switched to the other [darker] side, but I do not feel those arguments helping in any way, because of this dodgy image associated with gambling. (Pardon my French, but each time I read about escrows, I think of escrocs, or crooks, which reinforces this image! Actually, this name derives from the Old French escroue, but the modern meaning of écroué is sent to jail, which brings us back to the same feeling…)

Furthermore, it sounds like both a weak notion, since it implies an almost sure loss for the bookmaker, plus coherency holds for any prior distribution, including Dirac masses!, and a frequentist one, in that it looks at all possible values of the parameter (in a statistical framework). It also turns errors into monetary losses, taking them at face value. Which sounds also very formal to me.

But the most fundamental problem I have with this approach is that, from a Bayesian perspective, it does not bring any evaluation or ranking of priors, and in particular does not help in selecting or eliminating some. By behaving like a minimax principle, it does not condition on the data and hence does not evaluate the predictive properties of the model in terms of the data, e.g. by comparing pseudo-data with real data.

 While I see no reason to argue in favour of p-values or minimax decision rules, I am at a loss in understanding the examples in How to not gamble if you must. In the first case, i.e., when dismissing the α-level most powerful test in the simple vs. simple hypothesis testing case, the argument (in Example 4) starts from the classical (Neyman-Pearsonist) statistician favouring the 0.05-level test over others. Which sounds absurd, as this level corresponds to a given loss function, which cannot be compared with another loss function. Even though the authors chose to rephrase the dilemma in terms of a single 0-1 loss function and then turn the classical solution into the choice of an implicit variance-dependent prior. Plus force the poor Pearsonist to make a wager represented by the risk difference. The whole sequence of choices sounds both very convoluted and far away from the usual practice of a classical statistician… Similarly, when attacking [in Section 5.2] the minimax estimator in the Bernoulli case (for the corresponding proper prior depending on the sample size n), this minimax estimator is admissible under quadratic loss and still a Dutch book argument applies, which in my opinion definitely argues against the Dutch book reasoning. The way to produce such a domination result is to mix two Bernoulli estimation problems for two different sample sizes but the same parameter value, in which case there exist [other] choices of Beta priors and a convex combination of the risks functions that lead to this domination. But this example [Example 6] mostly exposes the artificial nature of the argument: when estimating the very same probability θ, what is the relevance of adding the risks or errors resulting from using two estimators for two different sample sizes. Of the very same probability θ. I insist on the very same because when instead estimating two [independent] values of θ, there cannot be a Stein effect for the Bernoulli probability estimation problem, that is, any aggregation of admissible estimators remains admissible. (And yes it definitely sounds like an exercise in frequentist decision theory!)