Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Sokolov, Vadim"'
We provide a statistical analysis of the recent controversy between Vladimir Kramnik (ex chess world champion) and Hikaru Nakamura. Hikaru Nakamura is a chess prodigy and a five-time United States chess champion. Kramnik called into question Nakamura
Externí odkaz:
http://arxiv.org/abs/2409.06739
Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative method based on quantiles that naturally calculates expected utility a
Externí odkaz:
http://arxiv.org/abs/2408.16101
Autor:
Polson, Nick, Sokolov, Vadim
Gauss proposed the problem of how to enumerate the number of solutions for placing $N$ queens on an $N\times N$ chess board, so no two queens attack each other. The N-queen problem is a classic problem in combinatorics. We describe a variety of Monte
Externí odkaz:
http://arxiv.org/abs/2407.08830
Autor:
Polson, Nick, Sokolov, Vadim
Negative probabilities arise primarily in physics, statistical quantum mechanics and quantum computing. Negative probabilities arise as mixing distributions of unobserved latent variables in Bayesian modeling. Our goal is to provide a link between th
Externí odkaz:
http://arxiv.org/abs/2405.03043
Autor:
Polson, Nick, Sokolov, Vadim
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 most statistical models, deep learning uses layers of semi-affine input t
Externí odkaz:
http://arxiv.org/abs/2310.06251
Publikováno v:
Entropy 25, no. 10: 1374
We propose a neural network-based approach to calculate the value of a chess square-piece combination. Our model takes a triplet (Color, Piece, Square) as an input and calculates a value that measures the advantage/disadvantage of having this piece o
Externí odkaz:
http://arxiv.org/abs/2307.05330
In this paper we propose the use of the generative AI methods in Econometrics. Generative methods avoid the use of densities as done by MCMC. They directrix simulate large samples of observables and unobservable (parameters, latent variables) and the
Externí odkaz:
http://arxiv.org/abs/2306.16096
Autor:
Polson, Nicholas G., Sokolov, Vadim
Bayesian Generative AI (BayesGen-AI) methods are developed and applied to Bayesian computation. BayesGen-AI reconstructs the posterior distribution by directly modeling the parameter of interest as a mapping (a.k.a. deep learner) from a large simulat
Externí odkaz:
http://arxiv.org/abs/2305.14972
In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available with conventional methods. In particular, our method is able to capture
Externí odkaz:
http://arxiv.org/abs/2301.00251
Autor:
Schultz, Laura, Sokolov, Vadim
Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Computer simulation models have many areas of applications, including socia
Externí odkaz:
http://arxiv.org/abs/2209.02163