stable processes related topics (22 resultados)

Hardcover. Condición: Fine. No Jacket. 1st Edition. BOOK IS IN VG/NF CONDITION. Looks brand new. Points are sharp and spine is square and intact. Pages are white with light discoloration on bottom from the shelf. There is no visible wear on the covers or binding of this book. Please see photos attached and feel free to reach out with questions about this title.

Condición: New. pp. 330.

Condición: New. pp. 330.

Condición: New. pp. 330.

Hardcover. Condición: Fine. Progress in Probability Volume 25. ix, 328, [1] p. 24 cm. Hardcover.

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Condición: Sehr gut. IX, 328 Seiten, Progress in Probability, Volume 25. Zust: Gutes Exemplar. Mängelexemplar-Stempel auf dem Fußschnitt. Schneller Versand und persönlicher Service - jedes Buch händisch geprüft und beschrieben - aus unserem Familienbetrieb seit über 25 Jahren. Eine Rechnung mit ausgewiesener Mehrwertsteuer liegt jeder unserer Lieferungen bei. Wir versenden mit der deutschen Post. Sprache: Englisch Gewicht in Gramm: 756 gebundene Ausgabe gebundene Ausgabe.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The Workshop on Stable Processes and Related Topics took place at Cor nell University in January 9-13, 1990, under the sponsorship of the Mathemat ical Sciences Institute. It attracted an international roster of probabilists from Brazil, Japan, Korea, Poland, Germany, Holland and France as well as the U. S. This volume contains a sample of the papers presented at the Workshop. All the papers have been refereed. Gaussian processes have been studied extensively over the last fifty years and form the bedrock of stochastic modeling. Their importance stems from the Central Limit Theorem. They share a number of special properties which facilitates their analysis and makes them particularly suitable to statistical inference. The many properties they share, however, is also the seed of their limitations. What happens in the real world away from the ideal Gaussian model The non-Gaussian world may contain random processes that are close to the Gaussian. What are appropriate classes of nearly Gaussian models and how typical or robust is the Gaussian model amongst them Moving further away from normality, what are appropriate non-Gaussian models that are sufficiently different to encompass distinct behavior, yet sufficiently simple to be amenable to efficient statistical inference The very Central Limit Theorem which provides the fundamental justifi cation for approximate normality, points to stable and other infinitely divisible models. Some of these may be close to and others very different from Gaussian models.

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Condición: Sehr gut. Zustand: Sehr gut | Seiten: 330 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

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Gebundene Ausgabe. Condición: Gut. IX, 328 S. : graph. Darst. ; 24 cm Original-Pappeinband m. Rücken- und Deckelbetitelung (kein Schutzumschlag). Ordentlich ausgesondertes Bibliotheksexemplar mit laminiertem Einband und mont. Inventarnummer a. Rücken in guter bis sehr guter Erhaltung. Signatur- / Entwidmungsstempel a. Vorsatz und Titel. 9783764334857 Werktäglicher Versand. Jede Lieferung m. ordentl. Rechnung und ausgew. MwSt. Der Versand erfolgt als Büchersendung / Einschreiben mit der Deutschen Post bzw. als Päckchen / Paket mit DHL. Die Lieferzeit ist abhängig von der Versandart und beträgt innerhalb Deutschlands 3-5 Tage, in der EU 5 - 12 Tage. KEIN Versand an Packstationen. Körperschaften und juristische Personen werden auf Wunsch per offener Rechnung beliefert. ***ATTENTION U.S. CUSTOMERS: Due to the new tariffs, we are unable to ship packages to the USA at this time. Thank you for understanding.*** Sprache: Englisch Gewicht in Gramm: 782.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Workshop on Stable Processes and Related Topics took place at Cor nell University in January 9-13, 1990, under the sponsorship of the Mathemat ical Sciences Institute. It attracted an international roster of probabilists from Brazil, Japan, Korea, Poland, Germany, Holland and France as well as the U. S. This volume contains a sample of the papers presented at the Workshop. All the papers have been refereed. Gaussian processes have been studied extensively over the last fifty years and form the bedrock of stochastic modeling. Their importance stems from the Central Limit Theorem. They share a number of special properties which facilitates their analysis and makes them particularly suitable to statistical inference. The many properties they share, however, is also the seed of their limitations. What happens in the real world away from the ideal Gaussian model The non-Gaussian world may contain random processes that are close to the Gaussian. What are appropriate classes of nearly Gaussian models and how typical or robust is the Gaussian model amongst them Moving further away from normality, what are appropriate non-Gaussian models that are sufficiently different to encompass distinct behavior, yet sufficiently simple to be amenable to efficient statistical inference The very Central Limit Theorem which provides the fundamental justifi cation for approximate normality, points to stable and other infinitely divisible models. Some of these may be close to and others very different from Gaussian models. 340 pp. Englisch.

Condición: New. Print on Demand pp. 340 23:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on White w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 340.

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Workshop on Stable Processes and Related Topics took place at Cor nell University in January 9-13, 1990, under the sponsorship of the Mathemat ical Sciences Institute. It attracted an international roster of probabilists from Brazil, Japan, Korea, Poland, Germany, Holland and France as well as the U. S. This volume contains a sample of the papers presented at the Workshop. All the papers have been refereed. Gaussian processes have been studied extensively over the last fifty years and form the bedrock of stochastic modeling. Their importance stems from the Central Limit Theorem. They share a number of special properties which facilitates their analysis and makes them particularly suitable to statistical inference. The many properties they share, however, is also the seed of their limitations. What happens in the real world away from the ideal Gaussian model The non-Gaussian world may contain random processes that are close to the Gaussian. What are appropriate classes of nearly Gaussian models and how typical or robust is the Gaussian model amongst them Moving further away from normality, what are appropriate non-Gaussian models that are sufficiently different to encompass distinct behavior, yet sufficiently simple to be amenable to efficient statistical inference The very Central Limit Theorem which provides the fundamental justifi cation for approximate normality, points to stable and other infinitely divisible models. Some of these may be close to and others very different from Gaussian models.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 340 pp. Englisch.