Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the mod...
Contains fulltext : 94188.pdf (preprint version ) (Open Access
The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offe...
In the field of Artificial Intelligence, Bayesian Networks (BN) are a well-known framework for reaso...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of ...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Bayesian networks are powerful tools for representing relations of dependence among variables of a d...
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is ...
Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which b...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesi...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
Bayesian networks are tools that were developed by the Artificial Intelligence and Statistic communi...
Contains fulltext : 94188.pdf (preprint version ) (Open Access
The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offe...
In the field of Artificial Intelligence, Bayesian Networks (BN) are a well-known framework for reaso...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of ...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Bayesian networks are powerful tools for representing relations of dependence among variables of a d...
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is ...
Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which b...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
As a compact graphical framework for representation of multivariate probabilitydistributions, Bayesi...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
Bayesian networks are tools that were developed by the Artificial Intelligence and Statistic communi...
Contains fulltext : 94188.pdf (preprint version ) (Open Access
The analysis of nominal data is often reduced to accumulation and description. Bayesian methods offe...
In the field of Artificial Intelligence, Bayesian Networks (BN) are a well-known framework for reaso...