peer reviewedChordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets.BIOMOD AR
This electronic version was submitted by the student author. The certified thesis is available in th...
The Glauber dynamics can efficiently sample independent sets almost uniformly at random in polynomia...
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Learning the structure of a graphical model from data is a common task in a wide range of practical ...
A recent paper [1] proposed a provably optimal, polynomial time method for performing near-isometric...
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AbstractWe discuss the problems to list, sample, and count the chordal graphs with edge constraints....
We propose to solve the combinatorial problem of finding the highest scoring Bayesian network stru...
This electronic version was submitted by the student author. The certified thesis is available in th...
The Glauber dynamics can efficiently sample independent sets almost uniformly at random in polynomia...
A graph is chordal if all its cycles of length greater than or equal to four contain a chord, i.e., ...
Graphical models are commonly used to encode conditional independence assumptions between random var...
In this paper we prove the so-called “Meek Conjecture”. In particular, we show that if a DAG H is an...
Probabilistic graphical models are graphical representations of probability distributions. Graphical...
In this paper, we derive optimality results for greedy Bayesian-network search algo-rithms that perf...
We present a new algorithmic approach for the task of finding a chordal Markov network structure tha...
We consider the problem of estimating the marginal independence structure of a Bayesian network from...
Learning the structure of a graphical model from data is a common task in a wide range of practical ...
A recent paper [1] proposed a provably optimal, polynomial time method for performing near-isometric...
Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digrap...
Unsupervised learning of graphical models is an important task in many domains. Although maximum lik...
AbstractWe discuss the problems to list, sample, and count the chordal graphs with edge constraints....
We propose to solve the combinatorial problem of finding the highest scoring Bayesian network stru...
This electronic version was submitted by the student author. The certified thesis is available in th...
The Glauber dynamics can efficiently sample independent sets almost uniformly at random in polynomia...
A graph is chordal if all its cycles of length greater than or equal to four contain a chord, i.e., ...