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Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing
Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing
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机译:通过可扩展的并行和分布式处理模拟大型经验样本来估计复合概率分布的技术
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摘要
Techniques for estimated compound probability distribution are described herein. Embodiments may include receiving, at a master node of a distributed system, a compound model specification comprising frequency models, severity models, and one or more adjustment functions, wherein at least one model of the frequency models and the severity models depend on one or more regressor and distributing the compound model specification to worker nodes of the distributed system, each of the worker nodes to at least generate a portion of samples for use in predicting compound distribution model estimates. Embodiments may also include predicting the compound distribution model estimates based on the sample portions of aggregate values and adjusted aggregate values.
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