Francesco Battaglia – författare

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

Il testo presenta gli sviluppi matematici e statistici relativi alo studio dei fenomeni dipendenti dal tempo, con l'obiettivo di mettere in grado il lettore di comprendere ed utilizzare i metodi di previsione statistica per le serie temporali, le catene di Markov, i processi di punti. L'attenzione è posta sia sugli aspetti strutturali probabilistici, illustrandoli nel dominio temporale e in quello frequenziale, sia su quelli statistici relativi all'inferenza .Il libro nasce come testo per corsi di previsione statistica sia di laurea triennale sia di specialistica, con due diversi percorsi, ma risulta utile come testo di riferimento e di guida all'approfondimento anche per corsi avanzati e di dottorato, e per i ricercatori impegnati, in ogni campo, nell'attività di previsione .