67 citations · 70 across the 8 of their papers we have counts for
8 papers
Generative Bayesian Computation for Maximum Expected Utility
Nick Polson, Fabrizio Ruggeri, Vadim Sokolov
Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative…
Counting Queens
Nick Polson, Vadim Sokolov
Gauss proposed the problem of how to enumerate the number of solutions for placing queens on an chess board, so no two queens attack each other. The N-queen problem…
Deep Learning: A Tutorial
Nick Polson, Vadim Sokolov
Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Rather than using shallow additive architectures common to mos…
Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs
Laura Schultz, Vadim Sokolov
Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Compu…
On the Probability of Magnus Carlsen reaching 2900
Sohan Bendre, Shiva Maharaj, Nick Polson +1
How likely is it that Magnus Carlsen will achieve an Elo rating of ? This has been a goal of Magnus and is of great current interest to the chess community. Our paper uses pr…
Bayesian Learning: A Selective Overview
Yu Lin Hsu, Chu Chuan Jeng, Pavithra Sripathanallur Murali +4
This paper presents an overview of some of the concepts of Bayesian Learning. The number of scientific and industrial applications of Bayesian learning has been growing in size rap…