This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating.N. B. Professor Lauritzen was based at Aarlborg University when this article was first published. The full-text of this article is not available in ORA. Citation: Lauritzen, S. L. & Sheehan, N. A. (2003). 'Graphical models for genetic analyses', Statistical Sci...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
mapping of complex traits due to their low genetic heterogeneity. Usually these pedigrees contain a ...
Population genetics theory is primarily based on mathematical models in which spatial complexity and...
This paper introduces graphical models as a natural environment in which to formulate and solve prob...
Abstract. This paper introduces graphical models as a natural environment in which to formulate and ...
Analyses of genetic data on groups of related individuals, or pedigrees, frequently require the calc...
Graphical modelling in its modern form was pioneered by Lauritzen and Wer-muth [43] and Pearl [55] i...
The genetic algorithm (GA) is an adaptive search algorithm used in high-dimensional optimization pro...
International audienceProbabilistic graphical models have been widely recognized as a powerful forma...
Genetic algorithms are traditionally formulated as search procedures that make use of selection, cro...
Recently, a great effort in microarray data analysis is directed towards the study of the so-calle...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
Pairwise linkage disequilibrium, haplotype blocks, and recombination hotspots provide only a partial...
In this work, we look at a two-sample problem within the framework of Gaussian graphical models. Whe...
With the abundance of increasingly complex and high dimensional data in many scientific disciplines,...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
mapping of complex traits due to their low genetic heterogeneity. Usually these pedigrees contain a ...
Population genetics theory is primarily based on mathematical models in which spatial complexity and...
This paper introduces graphical models as a natural environment in which to formulate and solve prob...
Abstract. This paper introduces graphical models as a natural environment in which to formulate and ...
Analyses of genetic data on groups of related individuals, or pedigrees, frequently require the calc...
Graphical modelling in its modern form was pioneered by Lauritzen and Wer-muth [43] and Pearl [55] i...
The genetic algorithm (GA) is an adaptive search algorithm used in high-dimensional optimization pro...
International audienceProbabilistic graphical models have been widely recognized as a powerful forma...
Genetic algorithms are traditionally formulated as search procedures that make use of selection, cro...
Recently, a great effort in microarray data analysis is directed towards the study of the so-calle...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
Pairwise linkage disequilibrium, haplotype blocks, and recombination hotspots provide only a partial...
In this work, we look at a two-sample problem within the framework of Gaussian graphical models. Whe...
With the abundance of increasingly complex and high dimensional data in many scientific disciplines,...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
mapping of complex traits due to their low genetic heterogeneity. Usually these pedigrees contain a ...
Population genetics theory is primarily based on mathematical models in which spatial complexity and...