Methodologies and Applications of Computational Statistics for Machine Intelligence
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Methodologies and Applications of Computational Statistics for Machine Intelligence

Debabrata Samanta (CHRIST (Deemed to be University), India), Raghavendra Rao Althar (QMS, First American India, Bangalore, India), Sabyasachi Pramanik (Haldia Institute of Technology, India) and Soumi Dutta (Institute of Engineering & Management, Kolkata, India)
Pages: 300|DOI: 10.4018/978-1-7998-7701-1
ISBN13: 9781799877011|ISBN10: 1799877019|EISBN13: 9781799877035|ISBN13 Softcover: 9781799877028
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Description

Computational statistics focus on devising an efficient methodology to obtain quantitative solutions for problems that are devised quantitatively. This subject brings together computational capability and statistical advanced thought processes together to solve some of the problems efficiently. Some of the interesting areas of the subject are optimization techniques in the process of statistical inference, algorithms of expectation maximization, Monte Carlo simulation to name a few. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. This book serves as a guide to the applications of new advances in computational statistics.

The book will cover topics on computational statistics, its methodologies, and applications. Computational arithmetic and its influence on computational statistics are analyzed and numerical algorithms in statistical application software are also explored. The basics of the computer systems are analyzed which forms the building block for algorithms, the statistical techniques that are behind simulation techniques are assessed, and the book also includes topics on linear algebra and its role in optimization techniques, the evolution of optimization techniques, and optimal utilization of computer resources. The statistical graphics role in data analysis is also investigated.

Additionally, statistical methods that are computationally intensive are studied. Computational inference and the computer model’s role in the design of experiments are also explored. The book also involves discussions on Bayesian analysis, survival analysis, and data mining in computational statistics and surveys innovative applications of computational statistics.

Table of Contents and List of Contributors

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