By Olivier Pourret
Bayesian Networks, the results of the convergence of synthetic intelligence with records, are transforming into in acceptance. Their versatility and modelling strength is now hired throughout various fields for the needs of study, simulation, prediction and diagnosis.This ebook offers a common creation to Bayesian networks, defining and illustrating the fundamental thoughts with pedagogical examples and twenty real-life case reviews drawn from a number fields together with medication, computing, traditional sciences and engineering.Designed to assist analysts, engineers, scientists and execs enjoying complicated determination approaches to effectively enforce Bayesian networks, this publication equips readers with confirmed ways to generate, calibrate, evaluation and validate Bayesian networks.The book:Provides the instruments to beat universal sensible demanding situations similar to the remedy of lacking enter information, interplay with specialists and choice makers, decision of the optimum granularity and dimension of the model. Highlights the strengths of Bayesian networks when additionally featuring a dialogue in their limitations.Compares Bayesian networks with different modelling ideas resembling neural networks, fuzzy good judgment and fault trees.Describes, for ease of comparability, the most good points of the key Bayesian community software program programs: Netica, Hugin, Elvira and Discoverer, from the viewpoint of the user.Offers a historic point of view at the topic and analyses destiny instructions for research.Written via major specialists with useful adventure of utilizing Bayesian networks in finance, banking, medication, robotics, civil engineering, geology, geography, genetics, forensic technology, ecology, and undefined, the e-book has a lot to supply either practitioners and researchers fascinated about statistical research or modelling in any of those fields.
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Extra info for Bayesian Networks: A Practical Guide to Applications (Statistics in Practice)
5 A causal mechanism is a mechanism describing the causal relations in the domain. 4 Hepar II: Modeling causal mechanisms. 5 Hepar II: Modeling the causes of the variable Hepatomegaly. through Reactive hepatitis node. The knowledge engineer explained to the expert that if there is another causal mechanism that leads directly to enlarged liver size due to taking Hepatotoxic medications, then the relationship between the node Hepatotoxic medications and the node Hepatomegaly should remain. 3 Elicitation of numerical parameters of the model Existing data sets of cases can signiﬁcantly reduce the knowledge engineering effort required to parameterize Bayesian networks.
It is generally accepted that building a BN involves three tasks : (1) identiﬁcation of the important variables and their values; (2) identiﬁcation and representation of the relationships between variables in the network structure; and (3) parameterization of the network, that is determining the CPTs associated with each network node. In our CHD application, step 3 is complicated by the fact that although many of the predictor variables are continuous, the BN software requires them to be discretized.
Concerned that clinicians would not use a full logistic regression model, they converted it into a points-based system where the clinician need only add the points from each risk factor, and read the risk off a graph. Therefore the authors had already discretized the continuous variables, making it straightforward to translate to a BN. ) We refer the reader to  for further discussion of the Busselton and PROCAM studies. There are at least four reasons to convert existing regression models to BNs.