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…

day three at ISBA 22

Still woke up early too early [to remain operational for the poster session], finalised the selection of our MASH 2022/3 students, then returned to the Jean-Drapeau pool, which was  even more enjoyable in a crisp bright blue morning (and hardly anyone in my lane).

Attended a talk by Li Ma, who reviewed complexifying stick-breaking priors on the weights and introduced a balanced tree stick mechanism (why same depth?) (with links to Jara & Hanson 2010 and Stefanucci & Canale 2021). Then I listened to Giovanni Rebaubo creating clustering Gibbs-type processes along graphs, I sorted of dozed and missed the point as it felt as if the graph turned from a conceptual connection into a physical one! Catherine Forbes talked about a sequential version of stochastic variational approximation (published in St&Co) exploiting the update-one-at-a-time feature of Bayesian construction, except that each step relies on the previous approximation, meaning that the final—if fin there is!—approximation can end up far away from the optimal stochastic variational approximation. Assessing the divergence away from the target (in real time and tight budget would be nice).

After a quick lunch where I tasted seaweed-shell gyozas (!), I went to the generalised Bayesian inference session on Gibbs posteriors, [sort of] making up for the missed SAVI workshop! With Alice Kirichenko (Warwick) deriving information complexity bounds under misspecification, plus deriving an optimal value for the [vexing] coefficient η [in the Gibbs posterior], and Jack Jewson (ex-Warwick), raising the issue of improper models within Gibbs posteriors, although the reference or dominating measure is a priori arbitrary in these settings. But missing the third talk, about Gibbs posteriors again, and Chris Homes’ discussion, to attend part of the Savage (thesis) Award, with finalists Marta Catalano (Warwick faculty), Aditi Shenvi (Warwick student), and John O’Leary (an academic grand-children of mine’s as Pierre Jacob was his advisor). What a disappointment to have to wait for Friday night to hear the outcome!

I must confess to some  (French-speaker) énervement at hearing Mon(t)-réal massacred as Mon-t-real..! A very minor hindrance though, when put in perspective with my friend and Warwick colleague Gareth Roberts forced to evacuate his hotel last night due to a fire in basement, fortunately unscathed but ruining Day 3 for him… (Making me realise the conference hotel itself underwent a similar event 14 years ago.)

dice and sticks

A quick weekend riddle from the Riddler about the probability of getting a sequence of increasing numbers from dice with an increasing number of faces, eg 4-, 6-, and 8-face dice. Which happens to be 1/4. By sheer calculation (à la Gauss) or simple enumération (à la R):

> for(i in 1:4)for(j in (i+1):6)F=F+(8-j)
> F/4/6/8
[1] 0.25

The less-express riddle is an optimisation problem related with stick breaking: given a stick of length one, propose a fraction a and win (1-a) if a Uniform x is less than one. Since the gain is a(1-a) the maximal average gain is associated with a=½. Now, if the remaining stick (1-a) can be divided when x>a, what is the sequence of fractions one should use when the gain is the length of the remaining stick? With two attempts only, the optimal gain is still ¼. And a simulation experiment with three attempts again returns ¼.

 

breaking sticks of various length

A riddle from the Riddler with a variation on the theme of breaking sticks: Given a stick of length L, what is the optimal manner to break said stick to achieve a maximal product of the individual lengths? While the pen & paper resolution is a one-line back-of-the-envelope calculation, with an impact of the length L, obviously,  a quick R code leads to an approximate solution:

mw=function(k=2,l=10,T=1e6){
  a=matrix(runif(T*k),k)
  for(i in 1:T)F=max(F,prod(l*a[,i]/sum(a[,i])))
  return(F)}

with increasing inaccuracy when L grows, obviously.