scholarly journals Monte Carlo methods for sensitivity analysis of Poisson-driven stochastic systems, and applications

2008 ◽  
Vol 40 (02) ◽  
pp. 293-320 ◽  
Author(s):  
Charles Bordenave ◽  
Giovanni Luca Torrisi
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Stochastic Models ◽  
Monte Carlo Methods ◽  
Stochastic Geometry ◽  
Stochastic Systems ◽  
Poisson Processes

We extend a result due to Zazanis (1992) on the analyticity of the expectation of suitable functionals of homogeneous Poisson processes with respect to the intensity of the process. As our main result, we provide Monte Carlo estimators for the derivatives. We apply our results to stochastic models which are of interest in stochastic geometry and insurance.

scholarly journals Monte Carlo methods for sensitivity analysis of Poisson-driven stochastic systems, and applications

2008 ◽  
Vol 40 (2) ◽  
pp. 293-320
Author(s):  
Charles Bordenave ◽  
Giovanni Luca Torrisi
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Stochastic Models ◽  
Monte Carlo Methods ◽  
Stochastic Geometry ◽  
Stochastic Systems ◽  
Poisson Processes

We extend a result due to Zazanis (1992) on the analyticity of the expectation of suitable functionals of homogeneous Poisson processes with respect to the intensity of the process. As our main result, we provide Monte Carlo estimators for the derivatives. We apply our results to stochastic models which are of interest in stochastic geometry and insurance.

ANALYSIS AND QUANTIFICATION OF DATA ASSIMILATION BASED ON SEQUENTIAL MONTE CARLO METHODS FOR WILDFIRE SPREAD SIMULATION

2010 ◽  
Vol 01 (04) ◽  
pp. 445-468 ◽  
Author(s):  
FENG GU ◽  
XIAOLIN HU
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Data Assimilation ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Sensor Data ◽  
Sequential Monte Carlo Methods ◽  
Systematic Analysis ◽  
Wildfire Spread ◽  
Simulation Results

Data assimilation is an important technique to improve simulation results by assimilating real time sensor data into a simulation model. A data assimilation framework based on Sequential Monte Carlo (SMC) methods for wildfire spread simulation has been developed in previous work. This paper provides systematic analysis and measurement to quantify the effectiveness and robustness of the developed data assimilation method. Measurement metrics are used to evaluate the robustness of SMC methods in data assimilation for wildfire spread simulation. Sensitivity analysis is carried out to examine the influences of important parameters to the data assimilation results. This work of analysis and quantification provides information to assess the effectiveness of the data assimilation method and suggests guidelines to further improve the data assimilation method for wildfire spread simulation.

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