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Bayesian Claims Reserving Methods in Non-life Insurance with Stan [electronic resource] : An Introduction / by Guangyuan Gao.

By: Contributor(s): Material type: TextTextPublisher: Singapore : Springer Singapore : Imprint: Springer, 2018Edition: 1st ed. 2018Description: XII, 205 p. 72 illus., 62 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811336096
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 519.5
LOC classification:
  • QA276-280
Online resources:
Contents:
Chapter1 Introduction -- Chapter2 Bayesian Fundamentals -- Chapter3 Advanced Bayesian Computation -- Chapter4 Bayesian Chain Ladder Models -- Chapter5 Bayesian Basis Expansion Models -- Chapter6 Multivariate Modelling Using Copulas -- Chapter7 Epilogue.
In: Springer Nature eBookSummary: This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes. .
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Chapter1 Introduction -- Chapter2 Bayesian Fundamentals -- Chapter3 Advanced Bayesian Computation -- Chapter4 Bayesian Chain Ladder Models -- Chapter5 Bayesian Basis Expansion Models -- Chapter6 Multivariate Modelling Using Copulas -- Chapter7 Epilogue.

This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes. .

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