搜索结果: 1-15 共查到“Priors”相关记录50条 . 查询时间(0.031 秒)
DESCRIPTION AND THE PROBLEM OF PRIORS
problem of priors Lewis-Skyrms signaling game evolutionary game theory
2016/6/12
Belief-revision models of knowledge describe how to update one's degrees of belief associated with hypotheses as one considers new evidence, but they typically do not say how probabilities become asso...
Verb Class Disambiguation Using Informative Priors
Informative Priors Class Disambiguation
2015/8/31
Levin’s (1993) study of verb classes is a widely used resource for lexical semantics. In her framework, some verbs, such as give, exhibit no class ambiguity. But other verbs, such as write, have
seve...
Minimax Bayes, asymptotic minimax and sparse wavelet priors
Minimax Decision theory Minimax Bayes estimation
2015/8/20
Pinsker(1980) gave a precise asymptotic evaluation of the minimax mean squared
error of estimation of a signal in Gaussian noise when the signal is known a priori
to lie in a compact ellipsoid in Hi...
CONJUGATE PRIORS FOR EXPONENTIAL FAMILIES
Random vector distribution natural parameter parameters
2015/7/15
Let X be a random vector distributed according to an exponential family
with natural parameter 0 E O.We characterize conjugate prior measures on O
through the property of linear posterior expectat...
Gibbs Sampling, Conjugate Priors and Coupling
Gibbs sampling conjugate clairvoyance coupling
2015/7/7
Gibbs Sampling, Conjugate Priors and Coupling。
de Finetti Priors using Markov chain Monte Carlo computations
Priors MCMC Contingency Tables Bayesian Inference Independence
2015/7/7
de Finetti Priors using Markov chain Monte Carlo computations。
Essays on the Managerial Implications of Differing Priors
Management Conflict and Resolution
2015/5/13
Essays on the Managerial Implications of Differing Priors.
Incorporating Boltzmann Machine Priors for Semantic Labeling in Images and Videos
computer vision machine learning image segmentation deep learning deep models
2014/12/18
Semantic labeling is the task of assigning category labels to regions in an image. For example, a scene may consist of regions corresponding to categories such as sky, water, and ground, or parts of a...
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/3/27
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
2013/3/14
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...
Scoring Bayesian Networks with Informative, Causal and Associative Priors
Scoring Bayesian Networks Informative Causal Associative Priors
2012/9/26
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data....
Bayesian inverse problems with non-conjugate priors
Rate of contraction posterior distribution nonparametric hypothesis testing.
2012/9/23
We investigate the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and severely ill-posed cases. A theorem is proved in a gene...
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.
2012/8/9
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
2012/8/9
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 ...