Výsledky vyhledávání - "Edera, Alejandro A."

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited

Externí odkaz: http://arxiv.org/abs/1407.8088

Zobrazit plný text záznamu

Report

Learning Markov networks with context-specific independences

Autor: Edera, Alejandro, Schlüter, Federico, Bromberg, Facundo

Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called independence-based lea

Externí odkaz: http://arxiv.org/abs/1307.3964

Zobrazit plný text záznamu

Report

Markov random fields factorization with context-specific independences

Autor: Edera, Alejandro, Bromberg, Facundo, Schlüter, Federico

Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersley-Clifford theorem uses these conditional independences to factorize

Externí odkaz: http://arxiv.org/abs/1306.2295

Zobrazit plný text záznamu

Report

The IBMAP approach for Markov networks structure learning

Autor: Schlüter, Federico, Bromberg, Facundo, Edera, Alejandro

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for

Externí odkaz: http://arxiv.org/abs/1301.3720

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání