Authors:
- Provides the reader with modeling and predictive tools of use in a number of applications of current interest
- Problems and solutions gradually increase in complexity throughout the brief so that learning can take place in easy steps
- New techniques allow better responses to sensor resource constraints by avoiding computationally prohibitive Markov chain Monte Carlo methods
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
Part of the book sub series: SpringerBriefs in Control, Automation and Robotics (BRIEFSCONTROL)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Department of Mechanical Engineering, Michigan State University, East Lansing, USA
Yunfei Xu, Jongeun Choi
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Department of Statistics and Probability, Michigan State University, East Lansing, USA
Sarat Dass, Tapabrata Maiti
About the authors
Sarat C. Dass received his Ph.D. and M.S. degrees in Statistics from Purdue University at West Lafayette, Indiana, US, in 1995 and 1998, respectively. He is currently Associate Professor at Universiti Teknologi Petronas in Malaysia. He received the B.Stat. (Hons) degree in Statistics from the Indian Statistical Institute in 1993. His current research interests include statistical inference for dynamical systems, statistical pattern recognition and image processing, and Bayesian methods with applications to various fields of engineering and technology. He is an Associate Editor for Sankhya B, Journal of the Indian Statistical Institute. He has received several awards for his interdisciplinary work including the Outstanding Statistical Application award from the American Statistical Association (ASA) and the Frank Wilcoxon award from Technometrics. Dr. Dass is a member of ASA and ISBA.
Tapabrata Maiti is a world class statistician, a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He has published research articles in top tier statistics journals such as Journal of the American Statistical Association, Annals of Statistics, Journal of the Royal Statistical Society, Series B, Biometrika, Biometrics etc. He has also published research articles in engineering, economics, genetics, medicine and social sciences. His research has been supported by the National Science Foundation and National Institutes of Health. He presented his work in numerous national and international meetings and in academic departments. Prof. Maiti served in editorial board of several statistics journals including journal of the American Statistical Association and journal of Agricultural, Environmental and Biological Statistics. He also served in several professional committees. Currently, he is a professor and the graduate director in the department of statistics and probability, Michigan State University. Prior to MSU, he was a tenured faculty member in the department of statistics, Iowa State University. Professor Maiti supervised several Ph.D. students and regularly teaches statistics and non-stat major graduate students.
Bibliographic Information
Book Title: Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks
Book Subtitle: Online Environmental Field Reconstruction in Space and Time
Authors: Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-3-319-21921-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-21920-2Published: 04 November 2015
eBook ISBN: 978-3-319-21921-9Published: 27 October 2015
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: XII, 115
Number of Illustrations: 41 b/w illustrations, 2 illustrations in colour
Topics: Control, Robotics, Mechatronics, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Artificial Intelligence, Signal, Image and Speech Processing, Communications Engineering, Networks