Markov random fields di rozanov y a (16 risultati)

Condizione: New.

Condizione: New.

Condizione: Gut. Auflage: 1982. 201 Seiten Einband mit leichten Lagerspuren, kleiner Besitzerstempel, Papier nachgebräunt, sonst sauber und gut erhalten. Sprache: Englisch Gewicht in Gramm: 472 24,8 x 1,7 x 1,9 cm, Gebundene Ausgabe.

Hardcover. Condizione: Good. 0387907084 Good; Hardcover; Withdrawn library copy with the standard library markings; Moderate wear to the covers; Library stamps to the endpapers; Text pages are clean & unmarked; Good binding with a straight spine; This book will be stored and delivered in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); Yellow covers with title in red lettering; 1982, Springer-Verlag Publishing; 201 pages; "Markov Random Fields (Applications of Mathematics)," by Y.A. Rozanov.…

Condizione: New. In English.

Condizione: very_good. This book is in Very good condition. There may be a few flaws like shelf wear and some light wear.

Condizione: New. pp. 216.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to the independence of the behavior of the process in the future from its behavior in the past, given knowledge of its state at the present moment. Extension to a generalized random process immediately raises nontrivial questions about the definition of a suitable' phase state,' so that given the state, future behavior does not depend on past behavior. Attempts to translate the Markov property to random functions of multi-dimensional 'time,' where the role of 'past' and 'future' are taken by arbitrary complementary regions in an appro priate multi-dimensional time domain have, until comparatively recently, been carried out only in the framework of isolated examples. How the Markov property should be formulated for generalized random functions of several variables is the principal question in this book. We think that it has been substantially answered by recent results establishing the Markov property for a whole collection of different classes of random functions. These results are interesting for their applications as well as for the theory. In establishing them, we found it useful to introduce a general probability model which we have called a random field. In this book we investigate random fields on continuous time domains. Contents CHAPTER 1 General Facts About Probability Distributions 1. 216 pp. Englisch.…

Condizione: New. Print on Demand pp. 216 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to the independence of the behavior of the process in the future from its behavior in the past, given knowledge of its state at the present moment. Extension to a generalized random process immediately raises nontrivial questions about the definition of a suitable' phase state,' so that given the state, future behavior does not depend on past behavior. Attempts to translate the Markov property to random functions of multi-dimensional 'time,' where the role of 'past' and 'future' are taken by arbitrary complementary regions in an appro priate multi-dimensional time domain have, until comparatively recently, been carried out only in the framework of isolated examples. How the Markov property should be formulated for generalized random functions of several variables is the principal question in this book. We think that it has been substantially answered by recent results establishing the Markov property for a whole collection of different classes of random functions. These results are interesting for their applications as well as for the theory. In establishing them, we found it useful to introduce a general probability model which we have called a random field. In this book we investigate random fields on continuous time domains. Contents CHAPTER 1 General Facts About Probability Distributions 1.…

Condizione: New. PRINT ON DEMAND pp. 216.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to .…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to the independence of the behavior of the process in the future from its behavior in the past, given knowledge of its state at the present moment. Extension to a generalized random process immediately raises nontrivial questions about the definition of a suitable' phase state,' so that given the state, future behavior does not depend on past behavior. Attempts to translate the Markov property to random functions of multi-dimensional 'time,' where the role of 'past' and 'future' are taken by arbitrary complementary regions in an appro priate multi-dimensional time domain have, until comparatively recently, been carried out only in the framework of isolated examples. How the Markov property should be formulated for generalized random functions of several variables is the principal question in this book. We think that it has been substantially answered by recent results establishing the Markov property for a whole collection of different classes of random functions. These results are interesting for their applications as well as for the theory. In establishing them, we found it useful to introduce a general probability model which we have called a random field. In this book we investigate random fields on continuous time domains. Contents CHAPTER 1 General Facts About Probability Distributions 1.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 216 pp. Englisch.…