This new, thoroughly revised and expanded 3 rd edition of a classic gives a comprehensive coverage of modern probability in a single book. It is a truly modern text, providing not only classical results but also material that will be important for future research. Much has been added to the previous edition, including eight entirely new chapters on subjects like random measures, Malliavin calculus, multivariate arrays, and stochastic differential geometry. Apart from important improvements and revisions, some of the earlier chapters have been entirely rewritten. To help the reader, the material has been grouped together into ten major areas, each arguably indispensable to any serious graduate student and researcher, regardless of their specialization.

Each chapter is largely self-contained and includes plenty of exercises, making the book ideal for self-study and for designing graduate-level courses and seminars in different areas and at different levels. Extensive notes and a detailed bibliography make it easy to go beyond the presented material if desired.

From the reviews of the first edition:

readers are likely to regard the book as an ideal reference. Indeed the monograph has the potential to become a (possibly even the) major reference book on large parts of probability theory for the next decade or more. M. Scheutzow, zbMATH

great edifice of material, clearly and ingeniously presented, without any non-mathematical distractions. Readers are in very capable hands. F. B. Knight, Mathemtical Reviews

this is precisely what Professor Kallenberg has attempted and he has accomplished it brilliantly... It is astonishing that a single volume of just over five hundred pages could contain so much material presented with complete rigor and still be at least formally self-contained..." R.K. Getoor, Metrika

From the reviews of the second edition:

This edition presents more material in the concise and elegant style of the former edition which by now has become a highly praised standard reference book for many areas of probability theory. M. Reiß, zbMATH

the monograph is a modern classic in probability theory every expert in one of the various topics covered by this monograph will reconsider his own point of view and gain deeper insight into his subject. Klaus D. Schmidt, Mathematical Reviews

Due to its size, and for the convenience of the reader, the print version of this book come in two separate volumes Presents a unified treatment of probability theory in a thoroughly revised and substantially extended 3rd edition of this classic text Invites the reader to go beyond the presented material via its extensive notes and detailed bibliography Includes supplementary material: sn.pub/extras

Autorentext

Olav Kallenberg received his Ph.D. in 1972 from Gothenburg University. After holding various temporary research positions in Sweden and abroad, he emigrated in 1986 to the US, where he became a professor of mathematics at Auburn University. In 1977 he became the second recipient ever of the prestigious Rollo Davidson Prize, in 1989 he was elected a Fellow of the IMS, and in 1991-1994 he served as the editor of robability Theory and Related Fields. Kallenberg is the author of the previous books "Foundations of Modern Probability", and "Probabilistic Symmetries and Invariance Principles" along with numerous research papers in all areas of probability.

Klappentext

This new, thoroughly revised and expanded 3rd edition of a classic gives a comprehensive coverage of modern probability in a single book. It is a truly modern text, providing not only classical results but also material that will be important for future research. Much has been added to the previous edition, including eight entirely new chapters on subjects like random measures, Malliavin calculus, multivariate arrays, and stochastic differential geometry. Apart from important improvements and revisions, some of the earlier chapters have been entirely rewritten. To help the reader, the material has been grouped together into ten major areas, each arguably indispensable to any serious graduate student and researcher, regardless of their specialization. Each chapter is largely self-contained and includes plenty of exercises, making the book ideal for self-study and for designing graduate-level courses and seminars in different areas and at different levels. Extensive notes and a detailed bibliography make it easy to go beyond the presented material if desired. From the reviews of the first edition: "...readers are likely to regard the book as an ideal reference. Indeed the monograph has the potential to become a (possibly even "the") major reference book on large parts of probability theory for the next decade or more." M. Scheutzow, zbMATH "...great edifice of material, clearly and ingeniously presented, without any non-mathematical distractions. Readers ... are in very capable hands." F. B. Knight, Mathemtical Reviews "... this is precisely what Professor Kallenberg has attempted ... and he has accomplished it brilliantly... It is astonishing that a single volume of just over five hundred pages could contain so much material presented with complete rigor and still be at least formally self-contained..." R.K. Getoor, Metrika From the reviews of the second edition: "This ... edition presents ... more material in the concise and elegant style of the former edition which by now has become a highly praised standard reference book for many areas of probability theory." M. Reiß, zbMATH "...the ... monograph is a modern classic in probability theory... ...every ... expert in one of the various topics covered by this monograph will reconsider his own point of view and gain deeper insight into his subject." Klaus D. Schmidt, Mathematical Reviews

Inhalt

Introduction and Reading Guide.- I.Measure Theoretic Prerequisites: 1.Sets and functions, measures and integration.- 2.Measure extension and decomposition.- 3.Kernels, disintegration, and invariance.- II.Some Classical Probability Theory: 4.Processes, distributions, and independence.- 5.Random sequences, series, and averages.- 6.Gaussian and Poisson convergence.- 7.Infinite divisibility and general null-arrays.- III.Conditioning and Martingales: 8.Conditioning and disintegration.- 9.Optional times and martingales.- 10.Predictability and compensation.- IV.Markovian and Related Structures: 11.Markov properties and discrete-time chains.- 12.Random walks and renewal processes.- 13.Jump-type chains and branching processes.- V.Some Fundamental Processes: 14.Gaussian processes and Brownian motion.- 15.Poisson and related processes.- 16.Independent-increment and Lévy processes.- 17.Feller processes and semi-groups.- VI.Stochastic Calculus and Applications: 18.Itô integration and quadratic variation.- 19.Continuous martingales and Brownian motion.- 20.Semi-martingales and stochastic integration.- 21.Malliavin calculus.- VII.Convergence and Approximation: 22.Skorohod embedding and functional convergence.- 23.Convergence in distribution.- 24.Large deviations.- VIII.Stationarity, Symmetry and Invariance: 25.Stationary processes and ergodic theorems.- 26.Ergodic properties of Markov processes.- 27.Symmetric distributions and predictable maps.- 28.Multi-variate arrays and symmetries.- IX.Random Sets and Measures: 29.Local time, excursions, and additive functionals.- 30.Random mesures, smoothing and scattering.- 31.Palm and Gibbs kernels, local approximation.- X.SDEs, Diffusions, and Potential Theory: 32.Stochastic equations and martingale problems.- 33.One-dimensional SDEs and diffusions.- 34.PDE connections and potential theory.- 35.Stochasticdifferential geometry.- Appendices.- *1.Measurable maps.- 2.General topology.- 3.Linear spaces.- 4.Linear operators.- 5.Function and measure spaces.- 6.…