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Bayesian analysis of a marked point process: Application in seismic hazard assessment

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Bayesian analysis of a marked point process: Application in seismic hazard assessment

Content Provider	Springer Nature Link
Author	Rotondi, Renata Varini, Elisa
Copyright Year	2003
Abstract	Point processes are the stochastic models most suitable for describing physical phenomena that appear at irregularly spaced times, such as the earthquakes. These processes are uniquely characterized by their conditional intensity, that is, by the probability that an event will occur in the infinitesimal interval (t, t+Δt), given the history of the process up tot. The seismic phenomenon displays different behaviours on different time and size scales; in particular, the occurrence of destructive shocks over some centuries in a seismogenic region may be explained by the elastic rebound theory. This theory has inspired the so-called stress release models: their conditional intensity translates the idea that an earthquake produces a sudden decrease in the amount of strain accumulated gradually over time along a fault, and the subsequent event occurs when the stress exceeds the strength of the medium. This study has a double objective: the formulation of these models in the Bayesian framework, and the assignment to each event of a mark, that is its magnitude, modelled through a distribution that depends at timet on the stress level accumulated up to that instant. The resulting parameter space is constrained and dependent on the data, complicating Bayesian computation and analysis. We have resorted to Monte Carlo methods to solve these problems.
Starting Page	79
Ending Page	92
Page Count	14
File Format	PDF
ISSN	16182510
Journal	Statistical Methods and Applications
Volume Number	12
Issue Number	1
e-ISSN	1613981X
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data-constrained parameters Magnitude distribution Metropolis-Hastings algorithm Stress release model Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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