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Kategorie szczegółowe BISAC

Handbook of Probabilistic Models

ISBN-13: 9780128165140 / Angielski / Miękka / 2019 / 590 str.

Pijush Samui; Dieu Tien Bui; Subrata Chakraborty
Handbook of Probabilistic Models Pijush Samui Dieu Tie Subrata Chakraborty 9780128165140 Butterworth-Heinemann - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Handbook of Probabilistic Models

ISBN-13: 9780128165140 / Angielski / Miękka / 2019 / 590 str.

Pijush Samui; Dieu Tien Bui; Subrata Chakraborty
cena 731,67 zł
(netto: 696,83 VAT:  5%)

Najniższa cena z 30 dni: 724,19 zł
Termin realizacji zamówienia:
ok. 30 dni roboczych
Bez gwarancji dostawy przed świętami

Darmowa dostawa!
Kategorie:
Inne
Kategorie BISAC:
Computers > Information Technology
Computers > Data Science - Data Modeling & Design
Computers > Computer Simulation
Wydawca:
Butterworth-Heinemann
Język:
Angielski
ISBN-13:
9780128165140
Rok wydania:
2019
Ilość stron:
590
Waga:
0.77 kg
Wymiary:
22.86 x 15.24 x 3.02
Oprawa:
Miękka
Wolumenów:
01
Dodatkowe informacje:
Bibliografia

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

Dr. Pijush Samui is an Associate Professor, in the Department of Civil Engineering, at NIT Patna, India, and an Adjunct Professor at Ton Duc Thang University in Ho Chi Minh City, Vietnam. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore in 2008. His research interests include geohazards; earthquake geotechnical engineering; concrete technology, pile foundation and slope stability and application of AI in civil engineering. He has published more than 21 books, 32 book chapters and over 200 research papers in high impact factor journals as well as 30 conference proceedings. Dieu Tien Bui is Professor in GIS, in the Department of Business and IT at the University of South-Eastern Norway, Norway. He obtained a Master of Engineering, at Hanoi University of Mining and Geology, Hanoi, Vietnam, a PhD at the Department of Mathematical Sciences and Technology (IMT), Norwegian University, and was postdoctoral researcher in the same department. His research interests include GIS, remote sensing, artificial intelligence and machine learning. He published journal and review articles, and book chapters. . Dr. Subrata Chakraborty is currently a Professor and former Head of Civil Engineering Department at the Indian Institute of Engineering Science and Technology, Shibpur. He is a fellow of the Indian National Academy Engineering. Prof. Chakraborty did his Bachelor in Civil Engineering from Bengal Engineering College, Shibpur, M. Tech. and PhD from IIT Kharagpur. He was a postdoctoral researcher at University of Cambridge, UK and University of Arizona, USA and Technical University of Aachen, Germany. In general, Prof. Chakraborty's research interests are in the field of computational mechanics under uncertainty, structural health monitoring, vibration control, composite mechanics etc. He has published extensively in peer reviewed journals, authored textbook and book chapters and reviewed research articles for various national and international journals. As an independent researcher, he has completed a number of research projects funded by various agencies and is also active in important industrial consultancy. While his inspiring teaching coupled with innate urge for intensive research has already established him as a distinguished academician at the national level, several awards and laurels have come his way. The Humboldt Fellowship for Experienced Researchers, the V. H. Joshi Award for Significant Contributions in Structural Dynamics, the INAE Young Engineer Award, the BOYSCAST Fellowship, and the Young Faculty Research Award deserve special mention. Professor Ravinesh Deo is an Associate Professor at University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence. He also serves as Associate Editor for two international journals: Stochastic Environmental Research and Risk Assessment and the ASCE Journal Hydrologic Engineering journal (USA). As an Applied Data Scientist with proven leadership in artificial intelligence, his research develops decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems especially using deep learning explainable AI, convolutional neural networks and long short-term memory networks. He was awarded internationally competitive fellowships including Queensland Government U.S. Smithsonian Fellowship, Australia-India Strategic Fellowship, Australia-China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and Endeavour Fellowship. He is a member of scientific bodies, won Publication Excellence Awards, Head of Department Research Award, Dean's Commendation for Postgraduate Supervision, BSc Gold Medal for Academic Excellence and he was the Dux of Fiji in Year 13 examinations. Professor Deo held visiting positions at United States Tropical Research Institute, Chinese Academy of Science, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University and National University of Singapore. He has undertaken knowledge exchange programs in Singapore, Japan, Europe, China, USA and Canada and secured international standing by researching innovative problems with global researchers. He has published Books with Springer Nature, Elsevier and IGI and over 190 publications of which over 140 are Q1 including refereed conferences, Edited Books and book chapters. Professor Deo's papers have been cited over 4,000 times with Google Scholar H-Index of 36 and a Field Weighted Citation Index exceeding 3.5.



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