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Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry… Expand Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference… Expand Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of… Expand We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First… Expand In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g… Expand Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under… Expand DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a… Expand Probabilistic models based on directed acyclic graphs (DAGs) have a long and rich tradition, which began with the geneticist… Expand Computational modelling of probability has become a major part of automated decision support systems. In this book, the principal… Expand From the Publisher:
Artificial "neural networks" are now widely used as flexible models for regression classification… Expand