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Condition: New. pp. 576.
Published by Springer US, Springer US, 1999
ISBN 10: 0306459566 ISBN 13: 9780306459566
Language: English
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - At this time when regulatory agencies are accepting and actively encouraging probabilistic approaches and the attribution of overall uncertainty among inputs to support Value of Information analyses, a comprehensive sourcebook on methods for addressing variability and uncertainty in exposure analysis is sorely needed. This need is adroitly met in Probabilistic Techniques in Exposure Assessment. A host of expert contributors provide a straightforward introduction to the practical tools for addressing variability and uncertainty in support of environmental and human health decision making. 151 graphs, plots, charts, and figures supplement a broad range of detailed and practical examples.
Published by Springer US, Springer US, 1999
ISBN 10: 0306459574 ISBN 13: 9780306459573
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - At this time when regulatory agencies are accepting and actively encouraging probabilistic approaches and the attribution of overall uncertainty among inputs to support Value of Information analyses, a comprehensive sourcebook on methods for addressing variability and uncertainty in exposure analysis is sorely needed. This need is adroitly met in Probabilistic Techniques in Exposure Assessment. A host of expert contributors provide a straightforward introduction to the practical tools for addressing variability and uncertainty in support of environmental and human health decision making. 151 graphs, plots, charts, and figures supplement a broad range of detailed and practical examples.
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Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Springer, 1999
ISBN 10: 1843090619 ISBN 13: 9781843090618
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Add to baskethardcover. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Published by Elsevier Science & Technology, Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. Englisch.
Published by Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Paperback. Condition: Brand New. 612 pages. 9.00x6.00x1.26 inches. In Stock. This item is printed on demand.
Published by Elsevier Science & Technology|Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
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Add to basketKartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of t.
Published by Elsevier Science & Technology, Butterworth-Heinemann, 2019
ISBN 10: 0128165146 ISBN 13: 9780128165140
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 162.36
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.