All about that [Detective] Bayes [seminar]

On 10 January 2023, at 14:00, Campus Pierre et Marie Curie (Sorbonne Université), Room 15.16-309, an All about that Bayes seminar presentation by Daniele Durante, visiting Paris Dauphine this month:

Daniele Durante (Bocconi University) – Detective Bayes: Bayesian nonparametric stochastic block modeling of criminal networks

Europol recently defined criminal networks as a modern version of the Hydra mythological creature, with covert structure and multifaceted evolutions. Indeed, relationships data among criminals are subject to measurement errors, structured missingness patterns, and exhibit a complex combination of an unknown number of core-periphery, assortative and disassortative structures that may encode key architectures of the criminal organization. The coexistence of these noisy block patterns limits the reliability of community detection algorithms routinely-used in criminology, thereby leading to overly-simplified and possibly biased reconstructions of organized crime topologies. In this seminar, I will present a number of model-based solutions which aim at covering these gaps via a combination of stochastic block models and priors for random partitions arising from Bayesian nonparametrics. These include Gibbs-type priors, and random partition priors driven by the urn scheme of a hierarchical normalized completely random measure. Product-partition models to incorporate criminals’ attributes, and zero-inflated Poisson representations accounting for weighted edges and secrecy strategies, will be also discussed. Collapsed Gibbs samplers for posterior computation are presented, and refined strategies for estimation, prediction, uncertainty quantification and model selection will be outlined. Results are illustrated in an application to an Italian Mafia network, where the proposed models unveil a structure of the criminal organization mostly hidden to state-of-the-art alternatives routinely used in criminology. I will conclude the seminar with ideas on how to learn the evolutionary history of the criminal organization from the relationship data among its criminals via a novel combination of latent space models for network data and phylogenetic trees.

JSM 2018 [#3]

As I skipped day #2 for climbing, here I am on day #3, attending JSM 2018, with a [fully Canadian!] session on (conditional) copula (where Bruno Rémillard talked of copulas for mixed data, with unknown atoms, which sounded like an impossible target!), and another on four highlights from Bayesian Analysis, (the journal), with Maria Terres defending the (often ill-considered!) spectral approach within Bayesian analysis, modelling spectral densities (Fourier transforms of correlations functions, not probability densities), an advantage compared with MCAR modelling being the automated derivation of dependence graphs. While the spectral ghost did not completely dissipate for me, the use of DIC that she mentioned at the very end seems to call for investigation as I do not know of well-studied cases of complex dependent data with clearly specified DICs. Then Chris Drobandi was speaking of ABC being used for prior choice, an idea I vaguely remember seeing quite a while ago as a referee (or another paper!), paper in BA that I missed (and obviously did not referee). Using the same reference table works (for simple ABC) with different datasets but also different priors. I did not get first the notion that the reference table also produces an evaluation of the marginal distribution but indeed the entire simulation from prior x generative model gives a Monte Carlo representation of the marginal, hence the evidence at the observed data. Borrowing from Evans’ fringe Bayesian approach to model choice by prior predictive check for prior-model conflict. I remain sceptic or at least agnostic on the notion of using data to compare priors. And here on using ABC in tractable settings.

The afternoon session was [a mostly Australian] Advanced Bayesian computational methods,  with Robert Kohn on variational Bayes, with an interesting comparison of (exact) MCMC and (approximative) variational Bayes results for some species intensity and the remark that forecasting may be much more tolerant to the approximation than estimation. Making me wonder at a possibility of assessing VB on the marginals manageable by MCMC. Unless I miss a complexity such that the decomposition is impossible. And Antonietta Mira on estimating time-evolving networks estimated by ABC (which Anto first showed me in Orly airport, waiting for her plane!). With a possibility of a zero distance. Next talk by Nadja Klein on impicit copulas, linked with shrinkage properties I was unaware of, including the case of spike & slab copulas. Michael Smith also spoke of copulas with discrete margins, mentioning a version with continuous latent variables (as I thought could be done during the first session of the day), then moving to variational Bayes which sounds quite popular at JSM 2018. And David Gunawan made a presentation of a paper mixing pseudo-marginal Metropolis with particle Gibbs sampling, written with Chris Carter and Robert Kohn, making me wonder at their feature of using the white noise as an auxiliary variable in the estimation of the likelihood, which is quite clever but seems to get against the validation of the pseudo-marginal principle. (Warning: I have been known to be wrong!)

Significance and artificial intelligence

As my sorry excuse of an Internet provider has been unable to fix my broken connection for several days, I had more time to read and enjoy the latest Significance I received last week. Plenty of interesting entries, once again! Even though, faithful to my idiosyncrasies, I must definitely criticise the cover (but you may also skip till the end of the paragraph!): It shows a pile of exams higher than the page frame on a student table in a classroom and a vague silhouette sitting behind the exams. I do not know whether or not this is intentional but the silhouette has definitely been added to the original picture (and presumably the exams as well!), because the seat and blackboard behind this silhouette show through it. If this is intentional, does that mean that the poor soul grading this endless pile of exams has long turned into a wraith?! If not intentional, that’s poor workmanship for a magazine usually apt at making the most from the graphical side. (And then I could go on and on about the clearly independent choice of illustrations by the managing editor rather than the author(s) of the article…) End of the digression! Or maybe not because there also was an ugly graph from Knowledge is Beautiful about the causes of plane crashes that made pie-charts look great… Not that all the graphs in the book are bad, far from it!

“The development of full artificial intelligence could spell the end of the human race.’ S. Hawkins

The central theme of the magazine is artificial intelligence (and machine learning). A point I wanted to mention in a post following the recent doom-like messages of Gates and Hawking about AIs taking over humanity à la Blade Runner… or in Turing’s test. As if they had not already impacted our life so much and in so many ways. And no all positive or for the common good. Witness the ultra-fast codes on the stock market. Witness the self-replicating and modifying computer viruses. Witness the increasingly autonomous military drones. Or witness my silly Internet issue, where I cannot get hold of a person who can tell me what the problem is and what the company is doing to solve it (if anything!), but instead have to listen to endless phone automata that tell me to press “1 if…” and “3 else”, and that my incident ticket has last been updated three days ago… But at the same time the tone of The Independent tribune by Hawking, Russell, Tegmark, and Wilczek is somewhat misguided, if I may object to such luminaries!, and playing on science fiction themes that have been repeated so many times that they are now ingrained, rather than strong scientific arguments. Military robots that could improve themselves to the point of evading their conceptors are surely frightening but much less realistic than a nuclear reaction that could not be stopped in a Fukushima plant. Or than the long-term impacts of genetically modified crops and animals. Or than the current proposals of climate engineering. Or than the emerging nano-particles.

“If we build systems that are game-theoretic or utility maximisers, we won’t get what we’re hoping for.” P. Norvig

The discussion of this scare in Significance does not contribute much in my opinion. It starts with the concept of a perfect Bayesian agent, supposedly the state of an AI creating paperclips, which (who?) ends up using the entire Earth’s resources to make more paperclips. The other articles in this cover story are more relevant, as for instance how AI moved from pure logic to statistical or probabilist intelligence. With Yee Whye Teh discussing Bayesian networks and the example of Google translation (including a perfect translation into French of an English sentence).