A Monte Carlo method for filtering a marked doubly stochastic Poisson process

DOI10.1007/S10260-007-0051-YzbMath1184.62162DBLPjournals/sma/Varini08OpenAlexW2070817776WikidataQ58209938 ScholiaQ58209938MaRDI QIDQ1039969

Elisa Varini

Publication date: 23 November 2009

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-007-0051-y

zbMATH Keywords

point processesMonte Carlo integrationstate-space modelnon-linear filter

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Continuous-time Markov processes on general state spaces (60J25) Monte Carlo methods (65C05) Seismology (including tsunami modeling), earthquakes (86A15) Sequential statistical analysis (62L10)

Related Items (3)

Change point analysis on the Corinth Gulf (Greece) seismicity ⋮ Inference for a class of partially observed point process models ⋮ Functional estimation of the random rate of a Cox process

Cites Work

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• Kalman filtering: with real-time applications.
• Bayesian analysis of a marked point process: application in seismic hazard assessment
• Finite state and discrete time approximation for filters.
• Sequential Monte Carlo Methods in Practice
• Simulation of nonhomogeneous Poisson processes with log linear rate function
• Markov Chains
• Bayesian Methods for Hidden Markov Models
• State Space and Unobserved Component Models
• On the unnormalized solution of the filtering problem with counting process observations

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