搜索结果: 1-12 共查到“理论统计学 graphical models”相关记录12条 . 查询时间(0.107 秒)
Learning the Structure of Mixed Graphical Models
Learning the Structure Mixed Graphical Models
2015/8/21
We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and di...
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Distributed Learning Gaussian Graphical Models Marginal Likelihoods
2013/4/28
We consider distributed estimation of the inverse covariance matrix, also called the concentration matrix, in Gaussian graphical models. Traditional centralized estimation often requires iterative and...
Feedback Message Passing for Inference in Gaussian Graphical Models
Belief propagation feedback vertex set Gaussian graphical models graphs with cycles Markov random field
2011/6/17
While loopy belief propagation (LBP) performs reasonably well for inference in some Gaussian
graphical models with cycles, its performance is unsatisfactory for many others. In particular for some
m...
Standard imsets for undirected and chain graphical models
conditional independence decomposable graph max-imal prime subgraph triangulation
2011/3/21
We derive standard imsets for undirected graphical models and chain graphical models. Standard imsets for undirected graphical models are described in terms of minimal triangulations for maximal prime...
Geometry of maximum likelihood estimation in Gaussian graphical models
Statistics Theory (math.ST) Algebraic Geometry (math.AG) Optimization and Control (math.OC)
2010/12/17
We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to find exact lower bounds on the number of observation...
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 ...
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...