搜索结果: 16-24 共查到“统计学 Graphical Models”相关记录24条 . 查询时间(0.054 秒)
Learning the Structure of Deep Sparse Graphical Models
Structure Deep Sparse Graphical Models deep belief networks
2010/3/9
Deep belief networks are a powerful way to model complex probability
distributions. However, learning the structure of a belief network,
particularly one with hidden units, is difficult. The Indian...
Dynamic Matrix-Variate Graphical Models
Bayesian Forecasting Dynamic Linear Models Gaussian Graphical Models Graphical Model Uncertainty Hyper-Inverse Wishart Distribution
2009/9/22
This paper introduces a novel class of Bayesian models for multivariate
time series analysis based on a synthesis of dynamic linear models and graphical
models. The synthesis uses sparse graphical m...
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
Approximate Bayesian Inference Bayes Factors Directed Acyclic Graphs EM Algorithm Graphical Models Markov Chain Monte Carlo
2009/9/21
A key problem in statistics and machine learning is inferring suitable
structure of a model given some observed data. A Bayesian approach to model
comparison makes use of the marginal likelihood of ...
Building hyper Dirichlet processes for graphical models
Hyper Markov law stick-breakingmeasure non-parametric prior decomposable graphical distribution covariance selection
2009/9/16
Graphical models are used to describe the conditional independence relations in multivariate data. They have been used for a variety of problems, including log-linear models (Liu and Massam, 2006), ne...
Inferring sparse Gaussian graphical models with latent structure
Gaussian graphical model Mixture model ℓ 1-penalization Model selection Variational inference EM algorithm
2009/9/16
Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional de...
Covariance estimation in decomposable Gaussian graphical models
Covariance estimation decomposable Gaussian graphical models
2010/3/18
Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are directly related to...
Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of
biological data in a variety of domains. But, what exactly are they and how do they work? How can we use...
Alternative parametrizations and reference priors for decomposable discrete graphical models
Clique Conjugate family Contingency table Cut Loglinearmodel Multinomial model Natural exponential family
2010/4/30
For a given discrete decomposable graphical model, we identify several alternative
parametrizations, and construct the corresponding reference priors for
suitable groupings of the parameters. Specif...
Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models
Covariance selection Reduced conditional sampling Variable selection
2010/4/29
Estimating a covariance matrix efficiently and discovering its structure are important
statistical problems with applications in many fields. This article takes a Bayesian
approach to estimate the c...
