By Olivier Pourret
Bayesian Networks, the results of the convergence of synthetic intelligence with information, are starting to be in reputation. Their versatility and modelling strength is now hired throughout quite a few fields for the needs of study, simulation, prediction and diagnosis.This booklet presents a basic creation to Bayesian networks, defining and illustrating the elemental thoughts with pedagogical examples and twenty real-life case reviews drawn from a number of fields together with medication, computing, normal sciences and engineering.Designed to aid analysts, engineers, scientists and pros enjoying advanced choice procedures to effectively enforce Bayesian networks, this publication equips readers with confirmed how you can generate, calibrate, assessment and validate Bayesian networks.The book:Provides the instruments to beat universal useful demanding situations resembling the therapy of lacking enter information, interplay with specialists and selection makers, choice of the optimum granularity and dimension of the model. Highlights the strengths of Bayesian networks when additionally providing a dialogue in their limitations.Compares Bayesian networks with different modelling recommendations similar to neural networks, fuzzy common sense and fault trees.Describes, for ease of comparability, the most gains of the main Bayesian community software program applications: Netica, Hugin, Elvira and Discoverer, from the viewpoint of the user.Offers a ancient point of view at the topic and analyses destiny instructions for research.Written by means of top specialists with useful adventure of utilising Bayesian networks in finance, banking, medication, robotics, civil engineering, geology, geography, genetics, forensic technological know-how, ecology, and undefined, the publication has a lot to provide 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)
Na¨ım, B. Marcot 34 CLINICAL DECISION SUPPORT the underlying medical database (which currently covers the areas of endocrinology and lymphoma diagnostics). Unfortunately too many of these applications appear to have been ‘one-offs’, with long development times using (mainly) expert elicitation and limited deployment. The lack of widespread adoption can be attributed to a wide range of factors, including (1) not embedding them in a more general decision support environment (rather than directly using BN software) that is more accessible for users who are domain experts but have little understanding of Bayesian networks and (2) the inability to easily adapt the parameterization of the network to populations other than those considered during the construction phase.
The author would like to thank her collaborators and co-authors, Marek Druzdzel, Hanna Wasyluk, Javier D´ıez and Carmen Lacave. Comments from Marek Druzdzel and Javier D´ıez improved the paper. 3 Decision support for clinical cardiovascular risk assessment Ann E. Nicholson, Charles R. Twardy, Kevin B. Korb and Lucas R. 1 Introduction In this chapter we describe a clinical decision support tool which uses Bayesian networks as the underlying reasoning engine. This tool, TakeHeartII, supports clinical assessment of risk for coronary heart disease (CHD).
In Hepar II only roughly 50% of all nodes could be approximated by Noisy-MAX . The experiment also studied the inﬂuence of noise in each of the three major classes of variables: (1) medical history, (2) physical examination, (3) laboratory tests, and (4) diseases, on the diagnostic performance. It seemed that noise in the results of laboratory tests was most inﬂuential for the diagnostic performance of our model. This can be explained by the high diagnostic value of laboratory tests. The diagnostic performance decreases with the introduction of noise.
Bayesian Networks: A Practical Guide to Applications (Statistics in Practice) by Olivier Pourret