Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing
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United States of America Patent
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Jun 18, 2019
Grant Date -
Mar 1, 2018
app pub date -
Nov 7, 2017
filing date -
Feb 19, 2014
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Abstract
Techniques for estimated compound probability distribution are described herein. Embodiments may include receiving a compound model specification comprising a frequency model and a severity model, the compound model specification including a model error comprising a frequency model error and a severity model error, and determining a number of frequency models and severity models to generate based on the received number of models to generate. Embodiments include generating a plurality of frequency models through perturbation of the frequency model according to the frequency model error, and generating a plurality of severity models through perturbation of the severity model according to the severity model error. Further, embodiments include dividing generation of a plurality of compound model samples among a plurality of distributed worker nodes, and receiving the plurality of compound model samples from the distributed worker nodes, and generating aggregate statistics from the plurality of compound model samples.
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