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Anotace knihy

Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently.

The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results.

Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.

Parametry knihy

Zařazení knihy Knihy v angličtině Mathematics & science Mathematics Probability & statistics

• Plný název: Sequential Analysis
• Podnázev: Hypothesis Testing and Changepoint Detection
• Autor: Alexander Tartakovsky, Igor Nikiforov, Michele Basseville
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 603
• EAN: 9780367740047
• ISBN: 0367740044
• ID: 33479441
• Nakladatelství: Taylor & Francis Ltd
• Hmotnost: 1120 g
• Rozměry: 254 × 178 mm
• Datum vydání: 18. December 2020

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