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Descrizione Autore: Pillonetto, Bisiacco
Editore: Esculapio
Data di Pubblicazione: 2025
ISBN: 9788893854689
Pagine: 256

This book focuses on Bayesian estimation problems for Information Engineering students, particularly those in Automation Engineering. It covers both off-line and on-line estimation, including real-time filtering and prediction. The text examines stochastic filtering, which estimates signals in dynamic systems with random disturbances. To achieve this, state-space models play a fundamental role in many parts of the book. The book traces the evolution of filtering techniques from Wiener and Kolmogorov’s stationary approach to Kalman’s state-space method. It begins with probability theory and Bayesian estimation fundamentals, and then moves on to Wiener and Kalman theories for discrete-time linear systems. The final chapters deal with nonlinear estimation using modern stochastic simulation techniques such as Markov chain Monte Carlo and particle filters, which have revolutionised the field of statistics in recent years and have found many applications in engineering and science. Throughout, the book balances theoretical concepts with practical examples and numerical illustrations. It concludes with exercises on the Kalman filter, useful for exam preparation. The content is suitable for advanced undergraduate and postgraduate students in the field.