Results 1  10
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21
Learning Bayesian networks: The combination of knowledge and statistical data
 Machine Learning
, 1995
"... We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simpl ..."
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Cited by 924 (34 self)
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We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user’s prior knowledge. In particular, a user can express his knowledge—for the most part—as a single prior Bayesian network for the domain. 1
Towards Compressing Web Graphs
 In Proc. of the IEEE Data Compression Conference (DCC
, 2000
"... In this paper, we consider the problem of compressing graphs of the link structure of the World Wide Web. We provide efficient algorithms for such compression that are motivated by recently proposed random graph models for describing the Web. ..."
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Cited by 81 (1 self)
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In this paper, we consider the problem of compressing graphs of the link structure of the World Wide Web. We provide efficient algorithms for such compression that are motivated by recently proposed random graph models for describing the Web.
Inferring Tree Models for Oncogenesis from Comparative Genome Hybridization Data
, 1998
"... Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to ..."
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Cited by 36 (1 self)
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Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to the occurrence of DNA gains and losses could be very useful in hunting cancer genes and in cancer diagnosis. We lay some mathematical foundations for inferring a model of tumor progression from a CGH data set. We consider a class of tree models that are more general than a path model that has been developed for colorectal cancer. We derive a tree model inference algorithm based on the idea of a maximumweight branching in a graph, and we show that under plausible assumptions our algorithm infers the correct tree. We have implemented our methods in software, and we illustrate with a CGH data set for renal cancer.
An Algorithmic Framework For Density Estimation Based Evolutionary Algorithms
, 1999
"... The direct application of statistics to stochastic optimization in evolutionary computation has become more important and present over the last few years. With the introduction of the notion of the Estimation of Distribution Algorithm (EDA), a new line of research has been named. The application are ..."
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Cited by 24 (5 self)
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The direct application of statistics to stochastic optimization in evolutionary computation has become more important and present over the last few years. With the introduction of the notion of the Estimation of Distribution Algorithm (EDA), a new line of research has been named. The application area so far has mostly been the same as for the classic genetic algorithms, being the binary vector encoded problems. The most important aspect in the new algorithms is the part where probability densities are estimated. In probability theory, a distinction is made between discrete and continuous distributions and methods. Using the rationale for density estimation based evolutionary algorithms, we present an algorithmic framework for them, named IDEA. This allows us to define such algorithms for vectors of both continuous and discrete random variables, combining techniques from existing EDAs as well as density estimation theory. The emphasis is on techniques for vectors of continuous random variables, for which we present new algorithms in the field of density estimation based evolutionary algorithms, using two different density estimation models.
Arborescence optimization problems solvable by Edmonds’ algorithm
 Theoretical Computer Science
, 2003
"... Abstract. We consider a general class of optimization problems regarding spanning trees in directed graphs (arborescences). We present an algorithm for solving such problems, which can be considered as a generalization of Edmonds ’ algorithm for the solution of the minimumcost arborescence problem. ..."
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Cited by 12 (1 self)
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Abstract. We consider a general class of optimization problems regarding spanning trees in directed graphs (arborescences). We present an algorithm for solving such problems, which can be considered as a generalization of Edmonds ’ algorithm for the solution of the minimumcost arborescence problem. The considered class of optimization problems includes as special cases the standard minimumcost arborescence problem, the bottleneck and the lexicographically optimal arborescence problem.
Construction of evolutionary tree models for renal cell carcinoma from comparative genomic hybridization data
 Cancer Res
, 2000
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Approximating Transitive Reductions for Directed Networks
"... Abstract. We consider minimum equivalent digraph problem, its maximum optimization variant and some nontrivial extensions of these two types of problems motivated by biological and social network applications. We provide 3approximation algorithms for all the minimiza2 tion problems and 2approxim ..."
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Cited by 2 (1 self)
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Abstract. We consider minimum equivalent digraph problem, its maximum optimization variant and some nontrivial extensions of these two types of problems motivated by biological and social network applications. We provide 3approximation algorithms for all the minimiza2 tion problems and 2approximation algorithms for all the maximization problems using appropriate primaldual polytopes. We also show lower bounds on the integrality gap of the polytope to provide some intuition on the final limit of such approaches. Furthermore, we provide APXhardness result for all those problems even if the length of all simple cycles is bounded by 5. 1
An Oil Pipeline Design Problem
 Operations Research
, 2003
"... We consider a given set of offshore platforms and onshore wells producing known (or estimated) amounts of oil to be connected to a port. Connections may take place directly between platforms, well sites, and the port, or may go through connection points at given locations. The configuration of the n ..."
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Cited by 1 (0 self)
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We consider a given set of offshore platforms and onshore wells producing known (or estimated) amounts of oil to be connected to a port. Connections may take place directly between platforms, well sites, and the port, or may go through connection points at given locations. The configuration of the network and sizes of pipes used must be chosen to minimize construction costs. This problem is expressed as a mixedinteger program, and solved both heuristically by Tabu Search and Variable Neighborhood Search methods and exactly by a branchandbound method. Two new types of valid inequalities are introduced. Tests are made with data from the South Gabon oil field and randomly generated problems.
A Global Structural EM Algorithm for a Model of Cancer Progression
"... Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein’s path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression ..."
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Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein’s path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising learning algorithms, and applying these to crosssectional data. Beerenwinkel et al. provided, what they coined, EMlike algorithms for Oncogenetic Trees (OTs) and mixtures of such. Given the small size of current and future data sets, it is important to minimize the number of parameters of a model. For this reason, we too focus on treebased models and introduce Hiddenvariable Oncogenetic Trees (HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOTmixtures). The algorithms are global in the sense that, during the Mstep, they find a structure that yields a global maximum of the expected complete loglikelihood rather than merely one that improves it. The algorithm for single HOTs performs very well on reasonablesized data sets, while that for HOTmixtures requires data sets of sizes obtainable only with tomorrow’s more costefficient technologies. 1
Approximating Optimum Branchings in Linear Time
"... We prove that maximum weight branchings in directed graphs can be approximated in time O(m) up to a factor of 1 − ǫ, where ǫ> 0 is an arbitrary constant. Key words: approximation algorithm, graph algorithm, branching 1 ..."
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We prove that maximum weight branchings in directed graphs can be approximated in time O(m) up to a factor of 1 − ǫ, where ǫ> 0 is an arbitrary constant. Key words: approximation algorithm, graph algorithm, branching 1