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- W2730785587 abstract "Abstract While Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian computation using distributed computing on Apache Spark across a cluster of computers. The distributed algorithm dramatically boosts the speed of Bayesian computation and expands the scope of applicability of Bayesian methods in insurance modeling. The empirical analysis applies a Bayesian hierarchical Tweedie model to a big data of 13 million insurance claim records. The distributed algorithm achieves as much as 65 times performance gain over the non-parallel method in this application. The analysis demonstrates that Bayesian methods can be of great value to large-scaled insurance predictive modeling." @default.
- W2730785587 created "2017-07-14" @default.
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- W2730785587 date "2017-07-06" @default.
- W2730785587 modified "2023-09-29" @default.
- W2730785587 title "BAYESIAN ANALYSIS OF BIG DATA IN INSURANCE PREDICTIVE MODELING USING DISTRIBUTED COMPUTING" @default.
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- W2730785587 doi "https://doi.org/10.1017/asb.2017.15" @default.
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