A backward selection procedure for approximating a discrete probability distribution by decomposable models

Francesco M. Malvestuto

Displaying similar documents to “A backward selection procedure for approximating a discrete probability distribution by decomposable models”

Tree and local computations in a cross–entropy minimization problem with marginal constraints

Francesco M. Malvestuto (2010)

Kybernetika

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In probability theory, Bayesian statistics, artificial intelligence and database theory the minimum cross-entropy principle is often used to estimate a distribution with a given set $P$ of marginal distributions under the proportionality assumption with respect to a given “prior” distribution $q$ . Such an estimation problem admits a solution if and only if there exists an extension of $P$ that is dominated by $q$ . In this paper we consider the case that $q$ is not given explicitly, but is specified...

On signed edge domination numbers of trees

Bohdan Zelinka (2002)

Mathematica Bohemica

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The signed edge domination number of a graph is an edge variant of the signed domination number. The closed neighbourhood $N_{G} [e]$ of an edge $e$ in a graph $G$ is the set consisting of $e$ and of all edges having a common end vertex with $e$ . Let $f$ be a mapping of the edge set $E (G)$ of $G$ into the set ${- 1, 1}$ . If $\sum_{x \in N [e]} f (x) \geq 1$ for each $e \in E (G)$ , then $f$ is called a signed edge dominating function on $G$ . The minimum of the values $\sum_{x \in E (G)} f (x)$ , taken over all signed edge dominating function $f$ on $G$ , is called the signed edge domination number...

The complexity of lexicographic sorting and searching

Juraj Wiedermann (1981)

Aplikace matematiky

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An asymptotically optimal sorting algorithm that uses $Θ (n (l o g n + k))$ component comparisons to lexicographically sort the set of $n$ $k$ -tuples is presented. This sorting algorithm builds the static data structure - the so called optimal lexicographic search tree - in which it is possible to perform member searching for an unknown $k$ -tuple in at most $[(l o g_{2} (n + 1)] + k - 1$ comparisons. The number of comparisons used by this search algorithm is optimal.