搜索结果: 1-15 共查到“统计学 priors”相关记录32条 . 查询时间(0.062 秒)
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/4/28
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...
Adaptive Priors based on Splines with Random Knots
Adaptive estimation bayesian non-parametric optimal contrac-tion rate spline random knots
2013/4/27
Splines are useful building blocks when constructing priors on nonparametric models indexed by functions. Recently it has been established in the literature that hierarchical priors based on splines w...
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Dependent Dirichlet Priors Optimal Linear Estimators Belief Net Parameters
2012/9/19
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
Hierarchical array priors for ANOVA decompositions
array-valued data Bayesian estimation cross-classi ed data factorial design MANOVA penalized regression tensor Tucker product sparse data.
2012/9/17
ANOVA decompositions are a standard method for describing and estimating heterogeneity among the means of a response variable across levels of multiple categorical factors. In such a decomposition, th...
The use of systems of stochastic PDEs as priors for multivariate models with discrete structures
Gaussian distribution multivariate stochastic PDEs discrete structures
2012/9/17
A challenge in multivariate problems with discrete structures is the inclusion of prior information that may di er in each separate structure. A particular example of this is seismic amplitude versus ...
Heavy tailed priors: an alternative to non-informative priors in the estimation of proportions on small areas
Survey Sampling Exponential Family Objective Robust Priors
2011/7/19
We explore the Cauchy and a new heavy tailed (Fuquene, Perez and Pericchi (2011)) priors to estimate proportions on small areas.
Grouped Variable Selection via Nested Spike and Slab Priors
Log-sum approximation Majorization-minimization algorithms
2011/7/6
In this paper we study grouped variable selection problems by proposing a specified prior, called the nested spike and slab prior, to model collective behavior of regression coefficients.
Estimation of covariance matrices based on hierarchical inverse-Wishart priors
Bayesian covariance estimation Skrinkage Hierarchical Inverse-Wishart prior
2011/7/5
This paper focuses on Bayesian shrinkage for covariance matrix estimation. We examine posterior properties and frequentist risks of Bayesian estimators based on new hierarchical inverse-Wishart priors...
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies
Bayesian variable selection generalized linear models Gaussian processes
2011/7/5
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data.
Besov priors for Bayesian inverse problems
Bayesian inverse problems Fernique-like theorem
2011/6/16
We consider the inverse problem of estimating a function u from
noisy, possibly nonlinear, observations. We adopt a Bayesian approach to the
problem and widen the existing theory, which is developed...
Compressible Priors for High-dimensional Statistics
linear inverse problem LASSO sparsity sparse regression ridge regression com-pressible prior compressive sensing instance optimality maximum a posteriori high-dimensional statistics order statistics
2011/3/18
We develop a principled way of identifying probability distributions whose independent and identically distributed (iid) realizations are compressible, i.e., can be approximated as sparse. We focus on...
Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors
Bayesian Sparsity-Path-Analysis Genetic Association Signal
2011/7/5
We explore the use of generalized t priors on regression coefficients to help understand the nature of association signal within "hit regions" of genome-wide association studies.
Generalized Species Sampling Priors with Latent Beta reinforcements
Statistics Theory (math.ST) Learning (cs.LG) Methodology (stat.ME)
2010/12/17
Many popular Bayesian Nonparametric priors can be characterized in terms of exchangeable species sampling sequences. One example is the Dirichlet Process prior, that has been increasingly used for mod...
Asymptotic admissibility of priors and elliptic differential equations
Asymptotic admissibility priors elliptic differential equations
2010/3/11
We evaluate priors by the second order asymptotic behaviour of the
corresponding estimators. Under certain regularity conditions, the risk di erences
between ecient estimators of parameters taking ...
Mirror averaging with sparsity priors
Mirror averaging progressive mixture sparsity aggregation of estimators oracleinequalities
2010/3/11
We consider the problem of aggregating the elements of a (possibly infinite) dictionary
for building a decision procedure, that aims at minimizing a given criterion. Along with the
dictionary, an in...