Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies
Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the st...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers a...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
For the computational analysis of biological problems—analyzing data, inferring networks and complex...
International audienceBayesian Networks: With Examples in R introduces Bayesian networks using a han...
This master's thesis deals with demonstration of various approaches to probabilistic inference in Ba...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
Probabilistic methods are the heart of machine learning. This chapter shows links between core princ...
Probabilistic reasoning, among methodologies used within the domain of artificial intelligence, is r...
Abstract. Building a probabilistic network for a real-life application is a difficult and time-consu...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespr...
Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the st...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers a...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
For the computational analysis of biological problems—analyzing data, inferring networks and complex...
International audienceBayesian Networks: With Examples in R introduces Bayesian networks using a han...
This master's thesis deals with demonstration of various approaches to probabilistic inference in Ba...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
Probabilistic methods are the heart of machine learning. This chapter shows links between core princ...
Probabilistic reasoning, among methodologies used within the domain of artificial intelligence, is r...
Abstract. Building a probabilistic network for a real-life application is a difficult and time-consu...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespr...
Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the st...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...