Archive for Gibbs priors

approximately Bayes [on Skye]

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , on May 28, 2026 by xi'an

Wow, what an exciting workshop in an equally exciting place! Strong themes were post-Bayes (Gibbs priors, martingale priors, predictive Bayes, &tc.) and deep neural network modelling. With animated discussions allowed by the free windows planned in the program. And the very early dinner at Sabhal Mòr Ostaig that let a long sunlit evening for impromptu Q&A’s [with a serving of lamb and another of haggis pie!]. Making me realise the large corpus of work I had missed in the past years on these topics, even though the satellite of BayesComp last year was already an eye opener. (Stay tuned for news about BayesComp 2027 & its mirror in Aussois!) The proposal in Jeff Miller’s discussion of Jeremias Knoblauch’s overview of post-Bayes [I’d rather favour another name!]  to consider directly likelihood values as the data was particularly appealing to me, while reminding me of the foundations of nested sampling. (Hopefully, a new perspective on uncertainty assessment for nested sampling is soon to be completed!)

On the non-academic side, the long days in The North helped with my running with above 90km bagged in the week (and no downpour on the runs). But little to my swimming since the water was cold enough to limit my laps to 5mn each time! Paradoxically the worst day was the one I chose for climbing the Inaccessible Pinnacle (as expanded in another ‘Og entry).

All about that [Detective] Bayes [seminar]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , on January 5, 2023 by xi'an
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.