collaborators

19 papers

math.ST2026

Martingale Posterior Predictive Coherence: Hausdorff Moment Hierarchy

Nicholas G. Polson, Daniel Zantedeschi

For an exchangeable Bernoulli sequence with de Finetti mixing measure Pi, the k-step predictive probability P(X_{n+1}=...=X_{n+k}=0 | F_n) equals the posterior expectation E[(1-the…

stat.ME2026

An Old Look at Empirical Bayes

Nicholas G. Polson, Vadim O. Sokolov, Daniel Zantedeschi

Dennis Lindley once said that there is only one thing worse than a frequentist, and that is an empirical Bayesian. The quip has the air of caricature, but its technical content is…

math.ST2026

Horseshoe Priors and MDP

Nick Polson, Vadim Sokolov, Daniel Zantedeschi

Carvalho (2010) established two foundational theorems for the horseshoe prior: tight two-sided logarithmic bounds on the marginal density near the origin (Theorem~1.1), and a super…

quant-ph2026

Bell's Inequality, Causal Bounds, and Quantum Bayesian Computation: A Unified Framework

Nick Polson, Vadim Sokolov, Daniel Zantedeschi

Bell inequalities characterize the boundary of the local-realist correlation polytope -- the set of joint probability distributions achievable by classical hidden-variable models.…

math.ST2026

Bayes, E-values and Testing

Nicholas G. Polson, Vadim Sokolov, Daniel Zantedeschi

E-values and E-processes (nonnegative supermartingales) provide anytime-valid evidence for sequential testing via Ville's inequality, yet their connection to Bayesian reasoning, re…

stat.ME2026

Synthetic Priors

Nick Polson, Vadim Sokolov

Bayesian inference in generalized linear models requires a prior on the coefficient vector . Practitioners naturally reason about response probabilities at specific covariate v…