Graphical models

Graphical Models

Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these appli...

Transforming Graphical System Models to Graphical Attack Models

Manually identifying possible attacks on an organisation is a complex undertaking; many different factors must be considered, and the resulting attack scenarios can be complex and hard to maintain as the organisation changes. System models provide a systematic representation of organisations that helps in structuring attack identification and can integrate physical, virtual, and social componen...

Graphical Models: Structure Learning

INTRODUCTION The article GRAPHICAL MODELS: PARAMETER LEARNING discussed the learning of parameters for a xed graphical model. In this article, we discuss the simultaneous learning of parameters and structure. Real-world applications of such learning abound and can be found in (e.g.) the Proceedings of the Conference on Uncertainty in Arti cial Intelligence (1991 and after). An index to software...

Reasoning with Graphical Models

Directed graphical models

for some functions f and g. Probabilistic graphical models are a way of representing conditional independence assumptions using graphs. Nodes represent random variables and lack of edges represent conditional independence assumptions, in a way which we will define below. There are many kinds of graphical model, but the two most popular are Bayesian (belief) networks1, which are based on directe...