day five at ISBA 22

Woke up even earlier today! Which left me time to work on switching to Leonard Cohen’s song titles for my slide frametitles this afternoon (last talk of the whole conference!), run once again to Mon(t) Royal as all pools are closed (Happy Canada Day!, except to “freedom convoy” antivaxxxers.) Which led to me meeting a raccoon by the side of the path (and moroons feeding wildlife).

Had an exciting time at the morning session, where Giacomo Zanella (formerly Warwick) talked on a mixture approach to leave-one-out predictives, with pseudo-harmonic mean representation, averaging inverse density across all observations. Better than harmonic? Some assumptions allow for finite variance, although I am missing the deep argument (in part due to Giacomo’s machine-gun delivery pace!) Then Alicia Corbella (Warwick) presented a promising entry into PDMP by proposing an automated zig-zag sampler. Pointing out on the side to Joris Bierkens’ webpage on the state-of-the-art PDMP methodology. In this approach, joint with with my other Warwick colleagues Simon Spencer and Gareth Roberts, the zig-zag sampler relies on automatic differentiation and sub-sampling and bound derivation, with “no further information on the target needed”. And finaly Chris Carmona presented a joint work with Geoff Nicholls that is merging merging cut posteriors and variational inference to create a meta posterior. Work and talk were motivated by a nice medieval linguistic problem where the latent variables impact the (convergence of the) MCMC algorithm [as in our k-nearest neighbour experience]. Interestingly using normalising [neural spline] flows. The pseudo-posterior seems to depend very much on their modularization rate η, which penalises how much one module influences the next one.

In the aft, I attended sort of by chance [due to a missing speaker in the copula session] to the end of a session on migration modelling, with a talk by Jason Hilton and Martin Hinsch focussing on the 2015’s mass exodus of Syrians through the Mediterranean,  away from the joint evils of al-Hassad and ISIS. As this was a tragedy whose modelling I had vainly tried to contribute to, I was obviously captivated and frustrated (leaning of the IOM missing migrant project!) Fitting the agent-based model was actually using ABC, and most particularly our ABC-PMC!!!

My own and final session had Gareth (Warwick) presenting his recent work with Jun Yang and Kryzs Łatuszyński (Warwick) on the stereoscopic projection improvement over regular MCMC, which involves turning the target into a distribution supported by an hypersphere and hence considering a distribution with compact support and higher efficiency. Kryzs had explained the principle while driving back from Gregynog two months ago. The idea is somewhat similar to our origaMCMC, which I presented at MCqMC 2016 in Stanford (and never completed), except our projection was inside a ball. Looking forward the adaptive version, in the making!

And to conclude this subjective journal from the ISBA conference, borrowing this title by (Westmount born) Leonard Cohen, “Hey, that’s not a way to say goodbye”… To paraphrase Bilbo Baggins, I have not interacted with at least half the participants half as much as I would have liked. But this was still a reunion, albeit in the new Normal. Hopefully, the conference will not have induced a massive COVID cluster on top of numerous scientific and social exchanges! The following days will tell. Congrats to the ISBA 2022 organisers for achieving a most successful event in these times of uncertainty. And looking forward the 2024 next edition in Ca’Foscari, Venezia!!!

 

Rapatriement des enfants et de leurs mères détenus dans des camps en Syrie

deduplication and population size estimation [discussion]

[Here is my discussion on the paper “A Unified Framework for De-Duplication and Population Size Estimation” by [my friends] Andrea Tancredi, Rebecca Steorts, and Brunero Liseo, to appear on the June 2020 issue of Bayesian Analysis. The deadline is 24 April. Discussions are to be submitted to BA as regular submissions.]

Congratulations to the authors, for this paper that expand the modelling of populations investigated by faulty surveys, a poor quality feature that applies to extreme cases like Syria casualties. And possibly COVID-19 victims.

The model considered in this paper, as given by (2.1), is a latent variable model which appears as hyper-parameterised in the sense it involves a large number of parameters and latent variables. First, this means it is essentially intractable outside a Bayesian resolution. Second, within the Bayesian perspective, it calls for identifiability and consistency questions, namely which fraction of the unknown entities is identifiable and which fraction can be consistently estimated, eventually severing the dependence on the prior modelling. Personal experiences with capture-recapture models on social data like drug addict populations showed me that prior choices often significantly drive posterior inference on the population size. Here, it seems that the generative distortion mechanism between registry of individuals and actual records is paramount.

“We now investigate an alternative aspect of the uniform prior distribution of λ given N.”

Since the practical application stressed in the title, namely some of civil casualties in Syria, interrogations take a more topical flavour as one wonders at the connection between the model and the actual data, between the prior modelling and the available prior information. It is however not the strategy adopted in the paper, which instead proposes a generic prior modelling that could be deemed to be non-informative. I find the property that conditioning on the list sizes eliminates the capture probabilities and the duplication rates quite amazing, reminding me indeed of similar properties for conjugate mixtures, although we found the property hard to exploit from a computational viewpoint. And that the hit-miss model provides computationally tractable marginal distributions for the cluster observations.

“Several records of the VDC data set represent unidentified victims and report only the date of death or do not have the first name and report only the relationship with the head of the family.”

This non-informative choice is however quite informative in the misreporting mechanism and does not address the issue that it presumably is misspecified. It indeed makes the assumption that individual label and type of record are jointly enough to explain the probability of misreporting the exact record. In practical cases, it seems more realistic that the probability to appear in a list depends on the characteristics of an individual, hence far from being uniform as well as independent from one list to the next. The same applies to the probability of being misreported. The alternative to the uniform allocation of individuals to lists found in (3.3) remains neutral to the reasons why (some) individuals are missing from (some) lists. No informative input is indeed made here on how duplicates could appear or on how errors are made in registering individuals. Furthermore, given the high variability observed in inferring the number of actual deaths covered by the collection of the two lists, it would have been of interest to include a model comparison assessment, especially when contemplating the clash between the four posteriors in Figure 4.

The implementation of a manageable Gibbs sampler in such a convoluted model is quite impressive and one would welcome further comments from the authors on its convergence properties, since it is facing a large dimensional space. Are there theoretical or numerical irreducibility issues for instance, created by the discrete nature of some latent variables as in mixture models?

deduplication and population size estimation [discussion opened]

A call (worth disseminating) for discussions on the paper “A Unified Framework for De-Duplication and Population Size Estimation” by [my friends] Andrea Tancredi, Rebecca Steorts, and Brunero Liseo, to appear on the June 2020 issue of Bayesian Analysis. The deadline is 24 April.

Data de-duplication is the process of detecting records in one or more datasets which refer to the same entity. In this paper we tackle the de-duplication process via a latent entity model, where the observed data are perturbed versions of a set of key variables drawn from a finite population of N different entities. The main novelty of our approach is to consider the population size N as an unknown model parameter. As a result, a salient feature of the proposed method is the capability of the model to account for the de-duplication uncertainty in the population size estimation. As by-products of our approach we illustrate the relationships between de-duplication problems and capture-recapture models and we obtain a more adequate prior distribution on the linkage structure. Moreover we propose a novel simulation algorithm for the posterior distribution of the matching configuration based on the marginalization of the key variables at population level. We apply our method to two synthetic data sets comprising German names. In addition we illustrate a real data application, where we match records from two lists which report information about people killed in the recent Syrian conflict.

Bayes for good

A very special weekend workshop on Bayesian techniques used for social good in many different sense (and talks) that we organised with Kerrie Mengersen and Pierre Pudlo at CiRM, Luminy, Marseilles. It started with Rebecca (Beka) Steorts (Duke) explaining [by video from Duke] how the Syrian war deaths were processed to eliminate duplicates, to be continued on Monday at the “Big” conference, Alex Volfonsky (Duke) on a Twitter experiment on the impact of being exposed to adverse opinions as depolarising (not!) or further polarising (yes), turning into network causal analysis. And then Kerrie Mengersen (QUT) on the use of Bayesian networks in ecology, through observational studies she conducted. And the role of neutral statisticians in case of adversarial experts!

Next day, the first talk of David Corlis (Peace-Work), who writes the Stats for Good column in CHANCE and here gave a recruiting spiel for volunteering in good initiatives. Quoting Florence Nightingale as the “first” volunteer. And presenting a broad collection of projects as supports to his recommendations for “doing good”. We then heard [by video] Julien Cornebise from Element AI in London telling of his move out of DeepMind towards investing in social impacting projects through this new startup. Including working with Amnesty International on Darfour village destructions, building evidence from satellite imaging. And crowdsourcing. With an incoming report on the year activities (still under embargo). A most exciting and enthusiastic talk!