Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science, Franco Taroni,Alex Biedermann,Silvia Bozza,Paolo G

Автор: Dana Kelly; Curtis Smith
Название: Bayesian Inference for Probabilistic Risk Assessment
ISBN: 1447127080 ISBN-13(EAN): 9781447127086
Издательство: Springer
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Цена: 121890.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book synthesizes significant recent advances in the use of risk analysis in many government agencies and private corporations, providing a Bayesian foundation for framing probabilistic problems and performing inference on these problems.

Автор: Marwala Tshilidzi
Название: Probabilistic Finite Element Model Updating Using Bayesian Statistics
ISBN: 1119153034 ISBN-13(EAN): 9781119153030
Издательство: Wiley
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Цена: 97100.00 T
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Описание: Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure.

The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: * Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.

* Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Автор: Harney
Название: Bayesian Inference
ISBN: 3319416421 ISBN-13(EAN): 9783319416427
Издательство: Springer
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Цена: 90370.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data.  New sections feature factorizing parameters, commuting parameters,  observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge.  Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.