A geometric view on learning Bayesian network structures
نویسندگان
چکیده
منابع مشابه
Geometric View on Learning Bayesian Network Structures
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structure, namely to represent every BN structure by a certain (uniquely determined) vector, called standard imset. The main result of the paper is that the set of standard imsets is the set of vertices (= extreme points) of a certain polytope. Motivated by the geometric view, we introduce the concept of the geo...
متن کاملA geometric view on learning Bayesian network structures
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structures, namely to represent every BN structure by a certain (uniquely determined) vector, called a standard imset. The main result of the paper is that the set of standard imsets is the set of vertices (= extreme points) of a certain polytope. Motivated by the geometric view, we introduce the concept of the ...
متن کاملLearning restricted Bayesian network structures
Bayesian networks are basic graphical models, used widely both in statistics and artificial intelligence. These statistical models of conditional independence structure are described by acyclic directed graphs whose nodes correspond to (random) variables in consideration. A quite important topic is the learning of Bayesian network structures, which is determining the best fitting statistical mo...
متن کاملLearning Equivalence Classes of Bayesian Network Structures
Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions that can be represented with the other. Many scoring criteria that are used to learn Bayesiannetwork structures from data are score equivalent; that is, these criteria do not distinguish among networks that are equiva...
متن کاملEvolutionary Methods for Learning Bayesian Network Structures
Bayesian networks (BN) are a family of probabilistic graphical models representing a joint distribution for a set of random variables. Conditional dependencies between these variables are symbolized by a Directed Acyclic Graph (DAG). Two classical approaches are often encountered when automaticaly determining an appropriate graphical structure from a database of cases,. The first one consists i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2010
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2010.01.014