Statistical inference for engineers and data scientists /

Statistical Inference for Engineers and Data Scientists A mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation a...

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Bibliographic Details
Main Authors:	Moulin, Pierre, 1963- (Author), Veeravalli, Venugopal V., 1963- (Author)
Format:	Book
Language:	English
Published:	Cambridge ; New York : Cambridge University Press, [2019]
Subjects:	Mathematical statistics. Engineering > Statistical methods. Quantitative research.

Table of Contents:

Introduction
Binary hypothesis testing
Multiple hypothesis testing
Composite hypothesis testing
Signal detection
Convex statistical distances
Performance bounds for hypothesis testing
Large deviations and error exponents for hypothesis testing
Sequential and quickest change detection
Detection of random processes
Bayesian parameter estimation
Minimum variance unbiased estimation
Information Inequality and Cramér-Rao lower bounds
Maximum likelihood estimation
Signal estimation.