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Handbook of Probabilistic Models

Handbook of Probabilistic Models - ISBN: 9780128165140
Prijs: € 185,30 (onder voorbehoud)
Beschikbaarheid: Levertijd tussen de 5 en 15 werkdagen. Geen retour recht.
Bindwijze: Boek, Paperback (08-10-2019)
Genre: Bedrijfsinformatietechnologie
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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.

  • Explains the application of advanced probabilistic models encompassing multidisciplinary research
  • Applies probabilistic modeling to emerging areas in engineering
  • Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems


Titel: Handbook of Probabilistic Models
Mediatype: Boek
Bindwijze: Paperback
Taal: Engels
Aantal pagina's: 590
Uitgever: Elsevier Science
Publicatiedatum: 2019-10-08
NUR: Bedrijfsinformatietechnologie
Afmetingen: 229 x 152
ISBN/ISBN13: 9780128165140
Intern nummer: 44647674

Biografie (woord)

Dr Ravinesh Deo obtained BSc (with Gold Medal) from University of the South Pacific, MSc (Honours) from University of Canterbury and PhD from Adelaide University including Graduate Certificate in Tertiary Teaching from University of Southern Queensland. At The University of Queensland Dr Deo worked as Postdoctoral Fellow followed by Principal Scientist with Queensland Government. Currently, he is Senior Lecturer with significant doctoral supervision and project leadership at University of Southern Queensland. Dr Deo held Senior Visiting Researcher positons at United States Smithsonian Tropical Research Institute, McGill University, Chinese Academy of Science, University of Tokyo, including Kyoto and Kyushu University, Peking University, National University of Singapore and Universidad de Alcal. Dr Deo is Associate Editor of ASCE Journal of Hydrologic Engineering, Editorial Board Member of Hydrology Research and Editor for Energies (SI). He won internationally prestigious fellowships and grants such as the Queensland Smithsonian Fellowship, Australia-China Young Scientist Award, Japan Society for Promotion of Science (JSPS) Fellowship, Chinese Academy of Science Fellowship and Australian Endeavour Fellowship. He teaches engineering mathematics, and leads artificial intelligence research whilst successfully supervised many doctoral and masters students. Dr Deo has published more than 150 peer reviewed papers that includes more than 100 Journal articles (high ranked), 2 Edited Books, Book Chapters and Conference papers in artificial intelligence, decision systems, energy, health informatics, and water and climate science.

Extra informatie

Explains engineering applications for a host of advanced probabilistic models, including the stochastic finite element method and copula-statistical models


1. Monte Carlo Simulation
2. Stochastic Optimization Method
3. Reliability Analysis
4. Stochastic Finite Element Method
5. Kalman Filter
6. Random matrix
7. Markov Chain
8. Gaussian Process Regression
9. Logistic regression
10. Geostatistics
11. Kriging
12. Bayesian inference
13. Bayesian updating
14. Probabilistic Neural Network
15. SVM, Relevance vector machine


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