Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Bhadra, Anindya"'
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:
Fang, Xiao, Bhadra, Anindya
In nonparameteric Bayesian approaches, Gaussian stochastic processes can serve as priors on real-valued function spaces. Existing literature on the posterior convergence rates under Gaussian process priors shows that it is possible to achieve optimal
Externí odkaz:
http://arxiv.org/abs/2312.07502
Autor:
Yarger, Drew, Bhadra, Anindya
Modeling of multivariate random fields through Gaussian processes calls for the construction of valid cross-covariance functions describing the dependence between any two component processes at different spatial locations. The required validity condi
Externí odkaz:
http://arxiv.org/abs/2312.05682
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical assumptions: (i) a homogeneous graph with a common network for all sub
Externí odkaz:
http://arxiv.org/abs/2310.18474
Autor:
Loría, Jorge, Bhadra, Anindya
From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process, when the network weights have bounded prior variance. Neal's result h
Externí odkaz:
http://arxiv.org/abs/2305.10664
Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally encodes the conditional independence relationship among variables. How
Externí odkaz:
http://arxiv.org/abs/2303.06914
Autor:
Loría, Jorge, Bhadra, Anindya
Consider the {$\ell_{\alpha}$} regularized linear regression, also termed Bridge regression. For $\alpha\in (0,1)$, Bridge regression enjoys several statistical properties of interest such as sparsity and near-unbiasedness of the estimates (Fan and L
Externí odkaz:
http://arxiv.org/abs/2212.02709
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference
We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend on individual-specific covariates. The individualized DAG structure of
Externí odkaz:
http://arxiv.org/abs/2210.08096
Autor:
Sagar, Ksheera, Bhadra, Anindya
The horseshoe prior, defined as a half Cauchy scale mixture of normal, provides a state of the art approach to Bayesian sparse signal recovery. We provide a new representation of the horseshoe density as a scale mixture of the Laplace density, explic
Externí odkaz:
http://arxiv.org/abs/2209.04510
Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tuning of prior hyper-parameters. Yet, the calculation of evidence has rema
Externí odkaz:
http://arxiv.org/abs/2205.01016