From the 2 of 15 linked papers with an AI index.
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Bayesian Prediction under Moment Conditioning
Nicholas G. Polson, Daniel Zantedeschi
The paper develops a Bayesian framework for prediction when only moment restrictions are available, using Kullback‑Leibler projection to define a conditional law for blocks of an e…
De Finetti + Sanov = Bayes: Exchangeable Prediction under Moment Constraints
Nicholas G. Polson, Daniel Zantedeschi
The paper studies prediction for exchangeable sequences when only empirical moment constraints are imposed, showing that the limiting predictive distribution is a Bayesian mixture…
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…
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…
E-Values, Bayes Risk, Dual Role of Markov's Inequality
Nicholas G. Polson, Daniel Zantedeschi
Two approaches to hypothesis testing, e-value testing and Bayes risk minimisation, both invoke Markov's inequality to control error probabilities. They differ in which distribution…
A New Look at Bayesian Testing
Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov +1
We identify the critical deviation scale governing Bayesian evidence accumulation in regular parametric testing. Under integrated Bayes risk with zero-one loss, the risk-optimal re…