Bayesian nonparametricsBayesian model selectionShrinkageLarge vector autoregressionNetwork representationConnectednessSeemingly unrelated regression (SUR) models are used in studying the interactions among economic variables of interest. In a high dimensional setting and when applied to large panel of time series...
The goal of this example is to demonstrate the comparative performance, via a simulation example, of (a) the variational Bayesian method, (b) the maximum likelihood/LS (12.6), and (c) the EM algorithm of Section 12.5 in the context of linear regression and in particular in the sparse mode...
Khardon (2012). Nonparametric Bayesian mixed-effect model: a sparse Gaussian process approach. http://arxiv.org/abs/1211.6653.Wang, Y. and Khardon, R. (2012). Nonparametric Bayesian Mixed-effect model: A sparse Gaussian process approach. http://arxiv.org/abs/1211. 6653....
(Jenattonet al.,2012). In genetics, Kim and Xing (2010) used it to exploit the tree structure of gene networks for multi-task regression. In topic models, Bleiet al.(2010) proposed a hierarchical model of latent variables based on Bayesian non-parametric methods to model hierarchies of ...
Adaptive variational Bayesian inference for sparse deep neural network (2020) BaiJ. et al. Efficient variational inference for sparse deep learning with theoretical guarantee BaulerB. et al. On deep learning as a remedy for the curse of dimensionality in nonparametric regression Ann. Statist. (2019...
A growing literature documents attempts to reconstruct gene networks by applying clustering methods [8, 9] and their more sophisticated variants such as statistical regression [10] and Bayesian networks [11]. While this line of work is important to help formulate hypotheses, there are many ...
Using a dense frequency grid, we formulate the problem as under-determined linear regression with a dual sparsity penalty, and develop an exact solution using the alternating direction method of multipliers (ADMoM). Simulations demonstrate that the developed technique outperforms spectrogram-based ...
Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Berno... M Zhou,H Chen,J Paisley,... - 《IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society》 被引量: 376...
Impose structured sparsity constraint for the low-rank subspace of the regression coefficients in the spatial domain. Abstract High spatial resolution hyperspectral images (HR-HSIs) have shown considerable potential in urban green infrastructure monitoring. A prevalent scheme to overcome spatial resolution ...
Nonparametric Bayesian adaptive dictionary learning was proposed in [40] for remote-sensing image fusion. Although SR gets great performance in image fusion, it still has two limitations in multi-modality image fusion. The first limitation is the most advanced Max-L1 sparse coefficients fusion rule ...