Computing Concise Representations of Semi-graphoid Independency Models

Autor:	Linda C. van der Gaag, Stavros Lopatatzidis
Rok vydání:	2015
Předmět:	Statistics::Machine Learning Basis (linear algebra) Conditional independence Joint probability distribution Improved algorithm Graphoid Representation (mathematics) Algorithm Mathematics
Zdroj:	Lecture Notes in Computer Science ISBN: 9783319208060 ECSQARU
DOI:	10.1007/978-3-319-20807-7_26
Popis:	The conditional independencies from a joint probability distribution constitute a model which is closed under the semi-graphoid properties of independency. These models typically are exponentially large in size and cannot be feasibly enumerated. For describing a semi-graphoid model therefore, a more concise representation is used, which is composed of a representative subset of the independencies involved, called a basis, and letting all other independencies be implicitly defined by the semi-graphoid properties; for computing such a basis, an appropriate algorithm is available. Based upon new properties of semi-graphoid models in general, we introduce an improved algorithm that constructs a smaller basis for a given independency model than currently existing algorithms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::750246a24e5353c532e197d599b4c0e7 https://doi.org/10.1007/978-3-319-20807-7_26 Zobrazit plný text záznamu