Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Chakraborty, Antik"'
Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn latent representations in modern multivariate data sets with complex dependency structures. Among these, the
Externí odkaz:
http://arxiv.org/abs/2404.17763
Autor:
Chakraborty, Antik, Walsh, Jonelle B., Strigari, Louis, Mallick, Bani K., Bhattacharya, Anirban
The orbital superposition method originally developed by Schwarzschild (1979) is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new relationships between the galaxy's global characteristics. Scientists are
Externí odkaz:
http://arxiv.org/abs/2404.03152
It has become increasingly common to collect high-dimensional binary response data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, multivariate probit (MVP) models are routinely used for inferences. Howe
Externí odkaz:
http://arxiv.org/abs/2106.02127
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-simi
Externí odkaz:
http://arxiv.org/abs/2004.08309
Publikováno v:
Journal of Machine Learning Research, 24(261), 1-46 (2023)
There is a rich literature on Bayesian methods for density estimation, which characterize the unknown density as a mixture of kernels. Such methods have advantages in terms of providing uncertainty quantification in estimation, while being adaptive t
Externí odkaz:
http://arxiv.org/abs/2003.07953
We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a carefully devised shrinkage prior on the matrix of regression coeffic
Externí odkaz:
http://arxiv.org/abs/1612.00877
We propose an efficient way to sample from a class of structured multivariate Gaussian distributions which routinely arise as conditional posteriors of model parameters that are assigned a conditionally Gaussian prior. The proposed algorithm only req
Externí odkaz:
http://arxiv.org/abs/1506.04778
Publikováno v:
Journal of the American Statistical Association, 2018 Mar 01. 113(521), 401-416.
Externí odkaz:
https://www.jstor.org/stable/45028533
Publikováno v:
Biometrika, 2016 Dec 01. 103(4), 985-991.
Externí odkaz:
https://www.jstor.org/stable/26363499
Autor:
Chakraborty, Antik1 (AUTHOR) antik015@purdue.edu, Ou, Rihui2 (AUTHOR), Dunson, David B.2 (AUTHOR)
Publikováno v:
Journal of the American Statistical Association. Sep2023, p1-12. 12p. 4 Illustrations, 1 Chart.