7 papers
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…
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.…
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…
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 va…
Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
Nick Polson, Vadim Sokolov
Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian predictive distributions…
Photons = Tokens: The Physics of AI and the Economics of Knowledge
Alec Litowitz, Nick Polson, Vadim Sokolov
Debates about artificial intelligence capabilities and risks are often conducted without quantitative grounding. This paper applies the methodology of MacKay (2009) -- who reframed…