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The Gibbs measure is a probability measure, which has been an important object in many problems of probability theory and statistical mechanics. It is the measure associated with the Hamiltonian of a physical system (a model) and generalizes the notion of a canonical ensemble. More importantly, when the Hamiltonian can be written as a sum of parts, the Gibbs measure has the Markov property (a certain kind of statistical independence), thus leading to its widespread appearance in many problems outside of physics such as biology, Hopfield networks, Markov networks, and Markov logic networks. Mor

Probability measures. --- Distribution (Probability theory) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Measures, Normalized --- Measures, Probability --- Normalized measures

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Probability theory is an actively developing branch of mathematics. It has applications in many areas of science and technology and forms the basis of mathematical statistics. This self-contained, comprehensive book tackles the principal problems and advanced questions of probability theory and random processes in 22 chapters, presented in a logical order but also suitable for dipping into. They include both classical and more recent results, such as large deviations theory, factorization identities, information theory, stochastic recursive sequences. The book is further distinguished by the inclusion of clear and illustrative proofs of the fundamental results that comprise many methodological improvements aimed at simplifying the arguments and making them more transparent. The importance of the Russian school in the development of probability theory has long been recognized. This book is the translation of the fifth edition of the highly successful and esteemed Russian textbook. This edition includes a number of new sections, such as a new chapter on large deviation theory for random walks, which are of both theoretical and applied interest. The frequent references to Russian literature throughout this work lend a fresh dimension and makes it an invaluable source of reference for Western researchers and advanced students in probability related subjects. Probability Theory will be of interest to both advanced undergraduate and graduate students studying probability theory and its applications. It can serve as a basis for several one-semester courses on probability theory and random processes as well as self-study. About the Author Professor Alexandr Borovkov lives and works in the Novosibirsk Academy Town in Russia and is affiliated with both the Sobolev Institute of Mathematics of the Russian Academy of Sciences and the Novosibirsk State University. He is one of the most prominent Russian specialists in probability theory and mathematical statistics. Alexandr Borovkov authored and co-authored more than 200 research papers and ten research monographs and advanced level university textbooks. His contributions to mathematics and its applications are widely recognized, which included election to the Russian Academy of Sciences and several prestigious awards for his research and textbooks.

Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities

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Self-similar processes are stochastic processes that are invariant in distribution under suitable time scaling, and are a subject intensively studied in the last few decades. This book presents the basic properties of these processes and focuses on the study of their variation using stochastic analysis. While self-similar processes, and especially fractional Brownian motion, have been discussed in several books, some new classes have recently emerged in the scientific literature.  Some of them are extensions of fractional Brownian motion (bifractional Brownian motion, subtractional Brownian motion, Hermite processes), while others are solutions to the partial differential equations driven by fractional noises. In this monograph the author discusses the basic properties of these new classes of  self-similar processes and their interrrelationship. At the same time a new approach (based on stochastic calculus, especially Malliavin calculus) to studying the behavior of the variations of self-similar processes has been developed over the last decade. This work surveys these recent techniques and findings on limit theorems and Malliavin calculus.

Calculus of variations. --- Calculus. --- Mathematics. --- Self-similar processes. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Stochastic processes. --- Random processes --- Selfsimilar processes --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistics, general. --- Probabilities --- Stochastic processes --- Distribution (Probability theory. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistics . --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk

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Il presente volume intende fornire un’introduzione alla probabilità e alle sue applicazioni, senza fare ricorso alla teoria della misura, per studenti dei corsi di laurea scientifici (in particolar modo di matematica, fisica e ingegneria).  Viene dedicato ampio spazio alla probabilità discreta, vale a dire su spazi finiti o numerabili. In questo contesto sono sufficienti pochi strumenti analitici per presentare la teoria in modo completo e rigoroso. L'esposizione è arricchita dall'analisi dettagliata di diversi modelli, di facile formulazione e allo stesso tempo di grande rilevanza teorica e applicativa, alcuni tuttora oggetto di ricerca.  Vengono poi trattate le variabili aleatorie assolutamente continue, reali e multivariate, e i teoremi limite classici della probabilità, ossia la Legge dei Grandi Numeri e il Teorema Limite Centrale, dando rilievo tanto agli aspetti concettuali quanto a quelli applicativi. Tra le varie applicazioni presentate, un capitolo è dedicato alla stima dei parametri in Statistica Matematica. Numerosi esempi sono parte integrante dell'esposizione. Ogni capitolo contiene una ricca selezione di esercizi, per i quali viene fornita la soluzione sul sito Springer dedicato al volume.

Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Mathematics. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistics, general. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Math --- Science --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistics .

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This work starts with the study of those limit theorems in probability theory for which classical methods do not work. In many cases some form of linearization can help to solve the problem, because the linearized version is simpler. But in order to apply such a method we have to show that the linearization causes a negligible error. The estimation of this error leads to some important large deviation type problems, and the main subject of this work is their investigation. We provide sharp estimates of the tail distribution of multiple integrals with respect to a normalized empirical measure and so-called degenerate U-statistics and also of the supremum of appropriate classes of such quantities. The proofs apply a number of useful techniques of modern probability that enable us to investigate the non-linear functionals of independent random variables. This lecture note yields insights into these methods, and may also be useful for those who only want some new tools to help them prove limit theorems when standard methods are not a viable option.

Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Distribution (Probability theory) --- Stochastic processes. --- U-statistics. --- Mann-Whitney statistics --- Sampling (Statistics) --- Random processes --- Probabilities --- Distribution functions --- Frequency distribution --- Characteristic functions --- Distribution (Probability theory. --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk