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pro vyhledávání: '"Srivastava, Sanvesh"'
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the post
Externí odkaz:
http://arxiv.org/abs/2304.11251
Autor:
Hing, Benjamin, Mitchell, Sara B., Filali, Yassine, Eberle, Maureen, Hultman, Ian, Matkovich, Molly, Kasturirangan, Mukundan, Johnson, Micah, Wyche, Whitney, Jimenez, Alli, Velamuri, Radha, Ghumman, Mahnoor, Wickramasinghe, Himali, Christian, Olivia, Srivastava, Sanvesh, Hultman, Rainbo
Publikováno v:
In Biological Psychiatry 1 December 2024 96(11):886-899
Data augmentation (DA) algorithms are widely used for Bayesian inference due to their simplicity. In massive data settings, however, DA algorithms are prohibitively slow because they pass through the full data in any iteration, imposing serious restr
Externí odkaz:
http://arxiv.org/abs/2109.08969
International Classification of Disease (ICD) codes are widely used for encoding diagnoses in electronic health records (EHR). Automated methods have been developed over the years for predicting biomedical responses using EHR that borrow information
Externí odkaz:
http://arxiv.org/abs/2108.01813
Autor:
Wang, Chunlei, Srivastava, Sanvesh
Divide-and-conquer Bayesian methods consist of three steps: dividing the data into smaller computationally manageable subsets, running a sampling algorithm in parallel on all the subsets, and combining parameter draws from all the subsets. The combin
Externí odkaz:
http://arxiv.org/abs/2105.14395
Autor:
Wang, Chunlei, Srivastava, Sanvesh
We show that the posterior distribution of parameters in a hidden Markov model with parametric emission distributions and discrete and known state space is asymptotically normal. The main novelty of our proof is that it is based on a testing conditio
Externí odkaz:
http://arxiv.org/abs/2105.14394
Varying coefficient models (VCMs) are widely used for estimating nonlinear regression functions for functional data. Their Bayesian variants using Gaussian process priors on the functional coefficients, however, have received limited attention in mas
Externí odkaz:
http://arxiv.org/abs/2006.00783
We devise survey-weighted pseudo posterior distribution estimators under two-stage informative sampling of both primary clusters and secondary nested units for a one-way analysis of variance (ANOVA) population generating model as a simple canonical c
Externí odkaz:
http://arxiv.org/abs/2004.06191
Monte Carlo algorithms, such as Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC), are routinely used for Bayesian inference in generalized linear models; however, these algorithms are prohibitively slow in massive data settings becau
Externí odkaz:
http://arxiv.org/abs/1911.07947
The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time-consuming in massive data applications because it require
Externí odkaz:
http://arxiv.org/abs/1806.07533