Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Gasco, Loretta"'
In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application
Externí odkaz:
http://arxiv.org/abs/1507.05079
Publikováno v:
Sankhyā: The Indian Journal of Statistics, Series B (1960-2002), 1997 Apr 01. 59(1), 84-95.
Externí odkaz:
https://www.jstor.org/stable/25052982
Publikováno v:
Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting June 6-10, 1998.
Externí odkaz:
https://doi.org/10.1093/oso/9780198504856.003.0031
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Advances in Software Engineering (978-3-642-10618-7); 2009, p160-167, 8p
Publikováno v:
Journal of Statistical Planning & Inference. Feb2003, Vol. 111 Issue 1/2, p23. 14p.
As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation o
Autor:
Kerry Back, Tomasz R. Bielecki, Christian Hipp, Shige Peng, Walter Schachermayer, Marco Frittelli, Wolfgang J. Runggaldier
This volume includes the five lecture courses given at the CIME-EMS School on'Stochastic Methods in Finance'held in Bressanone/Brixen, Italy 2003. It deals with innovative methods, mainly from stochastic analysis, that play a fundamental role in the