Zobrazeno 1 - 10
of 365
pro vyhledávání: '"Polson, Nicholas"'
Autor:
Datta, Jyotishka, Polson, Nicholas G.
Independent Component Estimation (ICE) has many applications in modern day machine learning as a feature engineering extraction method. Horseshoe-type priors are used to provide scalable algorithms that enables both point estimates via expectation-ma
Externí odkaz:
http://arxiv.org/abs/2406.17058
Autor:
Wang, Yuexi, Polson, Nicholas G.
Bayesian hierarchical models are commonly employed for inference in count datasets, as they account for multiple levels of variation by incorporating prior distributions for parameters at different levels. Examples include Beta-Binomial, Negative-Bin
Externí odkaz:
http://arxiv.org/abs/2402.09583
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
Autor:
Datta, Jyotishka, Polson, Nicholas G.
In Bayesian inference, the approximation of integrals of the form $\psi = \mathbb{E}_{F}{l(X)} = \int_{\chi} l(\mathbf{x}) d F(\mathbf{x})$ is a fundamental challenge. Such integrals are crucial for evidence estimation, which is important for various
Externí odkaz:
http://arxiv.org/abs/2305.03158
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
In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural network architecture, our methodology uses partial least squares for dime
Externí odkaz:
http://arxiv.org/abs/2207.02612
We use deep partial least squares (DPLS) to estimate an asset pricing model for individual stock returns that exploits conditioning information in a flexible and dynamic way while attributing excess returns to a small set of statistical risk factors.
Externí odkaz:
http://arxiv.org/abs/2206.10014