Bayesian analysis of big data in insurance predictive modeling using distributed computing
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Tag | 1 | 2 | Valor |
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LDR | 00000cab a2200000 4500 | ||
001 | MAP20170030553 | ||
003 | MAP | ||
005 | 20220911211739.0 | ||
008 | 170920e20170706bel|||p |0|||b|eng d | ||
040 | $aMAP$bspa$dMAP | ||
084 | $a6 | ||
100 | $0MAPA20140001262$aZhang, Yanwei | ||
245 | 1 | 0 | $aBayesian analysis of big data in insurance predictive modeling using distributed computing$cYanwei Zhang |
300 | $a19 p. | ||
520 | $aWhile 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. | ||
650 | 4 | $0MAPA20140022717$aBig data | |
650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
650 | 4 | $0MAPA20080592059$aModelos predictivos | |
773 | 0 | $wMAP20077000420$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association$x0515-0361$g01/09/2017 Volumen 47 Número 3 - septiembre 2017 , p. 943-961 |