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Model Learning for Probabilistic Simulation on Rare Events and Scenarios
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Title:
Model Learning for Probabilistic Simulation on Rare Events and Scenarios
Author:
Washio, Takashi
Subjects:
COMPUTERIZED SIMULATION
;
COVARIANCE SHIFT
;
Cybernetics
;
DATA MINING
;
FLOOD RISK ANALYSIS
;
KNOWLEDGE BASED SYSTEMS
;
LEARNING MACHINES
;
MARKOV PROCESSES
;
MATHEMATICAL MODELS
;
METOLOPOLIS ALGORITHM
;
MONTE CARLO METHOD
;
NATURAL DISASTERS
;
PATTERN RECOGNITION
;
PROBABILISTIC SIMULATION
;
PROBABILITY
;
RARE EVENT
;
REPLICA EXCHANGE MONTE CARLO
;
RISK ANALYSIS
;
SAMPLE GENERATION
;
STATISTICAL INFERENCE
;
Statistics and Probability
Description:
This project established a new methodology for probabilistic inference and prediction of rare and special events and/or scenarios based on the simulation models and the observed data that rarely contain rate events, applied it to a rainfall flood risk analysis of Chikugo river, Japan, and showed that it can generate various rainfall scenario that causes a flood. Rainfall pattern that causes a flood is generated by Replica Exchange Monte Carlo algorithm, and covariant shift phenomenon was corrected by placing more weight on the flood region. This work gives a general framework to cope with the problem of handling a complex and/or large scale system where a complete set of possible events and scenarios is hardly obtained.
Creation Date:
2015
Language:
English
Source:
DTIC Technical Reports
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