| 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 |
