From the 2 of 15 linked papers with an AI index.
15 papers
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…
Modal Exchangeability: Centered Symmetry and the Credal Architecture of Kripke Frames
Daniel Zantedeschi
We ask what happens when the index set carries modal structure, with possibilities organized into a Kripke frame. We define modal exchangeability as invariance under accessibility-…
Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective
Daniel Zantedeschi, Kumar Muthuraman
Stochastic gradient descent (SGD) is central to simulation optimization, stochastic programming, and online M-estimation, where sampling effort is a decision variable. We study 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…