In this paper we give a simple account of local computation of marginal probabilities for when the joint probability distribution is given in factored form and the sets of variables involved in the factors form a hypertree. Previous expositions of such local computation have emphasized conditional probability. We believe this emphasis is misplaced. What is essential to local computation is a factorization. It is not essential that this factorization be interpreted in terms of conditional probabilities. The account given here avoids the divisions required by conditional probabilities and generalizes readily to alternative measures of subjective probability, such Dempster-Shafer or Spohnian belief functions
In this paper we suggest a way of using the rules of System P to propagate lower bounds on condition...
貝氏因子(Bayes factor) 是貝氏統計方法中對於推論假設檢定(hypothesis testing)所用的一個 統計量。它的定義可以表成兩個資料的邊際機率(marginal probabil...
AbstractWhen applying any technique of multidimensional models to problems of practice, one always h...
In this paper we give a simple account of local computation of marginal probabilities for when the j...
This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, ...
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
We propose a novel bound on single-variable marginal probability distributions in factor graphs with...
summary:Marginal problem (see [Kel]) consists in finding a joint distribution whose marginals are eq...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
This short expository paper outlines applications of computer algebra to the implication problem of ...
Abstract. As inductive inference and machine learning methods in computer science see continued succ...
In the last decade, several architectures have been proposed for exact computation of marginals usi...
AbstractThe concept of conditional probability plays a fundamental role in probability theory. Just ...
We introduce a novel parameterization of distributions on hypergraphs based on the geometry of point...
Probability and algorithms enjoy an almost boisterous interaction that has led to an active, extensi...
In this paper we suggest a way of using the rules of System P to propagate lower bounds on condition...
貝氏因子(Bayes factor) 是貝氏統計方法中對於推論假設檢定(hypothesis testing)所用的一個 統計量。它的定義可以表成兩個資料的邊際機率(marginal probabil...
AbstractWhen applying any technique of multidimensional models to problems of practice, one always h...
In this paper we give a simple account of local computation of marginal probabilities for when the j...
This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, ...
Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowle...
We propose a novel bound on single-variable marginal probability distributions in factor graphs with...
summary:Marginal problem (see [Kel]) consists in finding a joint distribution whose marginals are eq...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
This short expository paper outlines applications of computer algebra to the implication problem of ...
Abstract. As inductive inference and machine learning methods in computer science see continued succ...
In the last decade, several architectures have been proposed for exact computation of marginals usi...
AbstractThe concept of conditional probability plays a fundamental role in probability theory. Just ...
We introduce a novel parameterization of distributions on hypergraphs based on the geometry of point...
Probability and algorithms enjoy an almost boisterous interaction that has led to an active, extensi...
In this paper we suggest a way of using the rules of System P to propagate lower bounds on condition...
貝氏因子(Bayes factor) 是貝氏統計方法中對於推論假設檢定(hypothesis testing)所用的一個 統計量。它的定義可以表成兩個資料的邊際機率(marginal probabil...
AbstractWhen applying any technique of multidimensional models to problems of practice, one always h...