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Debdeep Pati
Researcher at Texas A&M University
Publications - 107
Citations - 1912
Debdeep Pati is an academic researcher from Texas A&M University. The author has contributed to research in topics: Prior probability & Bayesian probability. The author has an hindex of 20, co-authored 97 publications receiving 1569 citations. Previous affiliations of Debdeep Pati include Duke University & Florida State University.
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Dirichlet–Laplace Priors for Optimal Shrinkage
TL;DR: In this paper, a new class of Dirichlet-laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation, is proposed, which can be expressed as global-local scale mixtures of Gaussians.
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Dirichlet-Laplace priors for optimal shrinkage
TL;DR: This article proposes a new class of Dirichlet–Laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation.
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Posterior consistency in conditional distribution estimation
TL;DR: Defining various topologies on the space of conditional distributions, this work provides sufficient conditions for posterior consistency focusing on a broad class of priors formulated as predictor-dependent mixtures of Gaussian kernels.
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Bayesian geostatistical modelling with informative sampling locations
TL;DR: A Bayesian approach is proposed, which models the locations using a log Gaussian Cox process, while modelling the outcomes conditionally on the locations as Gaussian with a Gaussian process spatial random effect and adjustment for the location intensity process.
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Posterior contraction in sparse Bayesian factor models for massive covariance matrices
TL;DR: In this article, a new class of continuous shrinkage priors is proposed for sparse Bayesian factor models with sparsity assumptions on the true covariance matrix, and the convergence rates of these priors are derived for high-dimensional covariance matrices.