Sparse Bayesian regression with automatic relevance
Dependent relevance determination for smooth and structured sparse regression
... criterion. Therefore, we included SCAD apart from lasso and employed AUC as the metric for support identification. The synthetic experiments generated true weight vectors from the proposed generative model, thus aiming ...
43
On Model Selection for Bayesian Networks and Sparse Logistic Regression
... of sparse feature selection and its application for learning Bayesian ...learning Bayesian networks, without considering the interactions between parents, the classic methods usually fail when such ...
68
Frequentist Properties of Bayesian Procedures for High-Dimensional Sparse Regression.
... logistic regression with binary responses, there is no available results on further theoretical properties of the posterior distribution beyond contraction rates, ...the Bayesian point of ...
142
PAC-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights
... putational Bayesian theory, see the monograph of Marin and Robert [40] for an introduction to Monte Carlo algorithms in Bayesian ...the Bayesian point of view for the variable selection problem was ...
21
Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler
... Logistic regression has been extensively used to perform classifica- tion in machine learning and signal/image ...processing. Bayesian for- mulations of this model with sparsity-inducing priors are ...
7
Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler
... Logistic regression has been extensively used to perform classifica- tion in machine learning and signal/image ...processing. Bayesian for- mulations of this model with sparsity-inducing priors are ...
8
Bayesian Tensor Regression
... We implement BTR, FTR, and Lasso on the vectorized tensor. As before, we present results for FTR with R = 10 (See additional discussion in Section 6 on FTR default setup). Point estimates for coefficients corresponding ...
31
Identification of nonlinear sparse networks using sparse Bayesian learning
... In this case, element sparse priors penalise the polynomial order. Simulations also show our method is superior to SBL, GSBL and kernel methods. Further developments should include two aspects. The first is to ...
7
BSGS: Bayesian Sparse Group Selection
... to sparse group selection, in which the sparse assumption is held for groups and the variables within ...a Bayesian group variable selection approach, the Group-wise Gibbs sampler (GWGS) Chen et ...
12
Priors on the Variance in Sparse Bayesian Learning; the demi-Bayesian Lasso
... certain sparse Bayesian regression ...between sparse Bayesian learning (SBL) models (Tipping, 2001) and the recently pro- posed Bayesian Lasso (Park and Casella, ...the ...
8
Sparse relative risk regression models
... a Bayesian MAP estimator with a particular prior on the parameters seems to merely reformulate the problem, rather than solving ...penalized regression methods start their exposition by assuming that the ...
17
Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... To summarize, the presented work moves forward to a compact, consistent, and automatic mechanism via Bayesian formulation of the ABC to find an optimal kernel and its hyperparameters simultaneously. As can ...
13
BLOCKWISE SPARSE REGRESSION
... Recently, Yuan an Lin (2004) proposed an interesting restriction called the grouped LASSO for the linear regression. The main advantage of the grouped LASSO is that one can make a group of regression ...
16
Simultaneous Bayesian Sparse Approximation With Structured Sparse Models
... the sparse representation vectors from the measurements vectors as opposed to solving for each signal ...simultaneous sparse Bayesian learning (SBL) for joint sparse approximation with two ...
14
Globally sparse PLS regression
... Globally Sparse PLS Regression Tzu-Yu Liu, Laura Trinchera, Arthur Tenenhaus, Dennis Wei and Alfred ...(PLS) regression combines dimensionality reduction and prediction using a latent variable ...for ...
12
Constrained sparse Galerkin regression
... Despite its promise, such an approach to system identification still suffers from certain limi- tations. One such limitation is illustrated by the quadratic constrained model identified for the shear-driven cavity flow ...
28
Communication-efficient Sparse Regression
... We devise a communication-efficient approach to distributed sparse regression in the high- dimensional setting. The key idea is to average “debiased” or “desparsified” lasso esti- mators. We show the ...
30
Bayesian Quantile Regression.
... the regression equation with the spline coefficients being linear, it is enough to track the posterior mean of the spline coefficients for the estimation pur- ...
122
Bayesian quantile regression
... a Bayesian treat- ment of quantile ...called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = ...
16
Bayesian Semiparametric Regression
... semiparametric Bayesian literature focuses on constructing nonparametric priors for error ...of regression coefficients in a linear model with errors generated by covariate independent mixtures of normals ...
54