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Full Description
There has been a rapidly growing interest in Bayesian methods among insurance practitioners in recent years, mainly because of their ability to generate predictive distributions and to rigorously incorporate expert opinion through prior probabilities. This book introduces modern Bayesian modeling techniques for actuarial and insurance applications. It first provides the necessary background in current actuarial practice and then presents Bayesian methods and MCMC. It includes advanced techniques, such as nonlinear modeling, as well as three chapters on model selection and averaging. The text features case studies using real actuarial and insurance data with computations in R and WinBUGS.
Contents
INTRODUCTION TO CURRENT PRACTICE Actuarial Background and Introduction to Insurance Traditional Models for Insurance Reserving and Pricing Case studies: Generalized Linear Models in Insurance Hierarchical Linear Models with Insurance Applications BAYESIAN LINEAR AND GENERALIZED LINEAR MODELS Introduction to Bayesian Statistics Inference in Bayesian Linear Models Introduction to Bayesian Computing: Markov Chain Monte Carlo Case Studies: Bayesian Generalized Linear Models in Insurance ADVANCED BAYESIAN METHODS AND CASE STUDIES Bayesian Semi-parametric Models Bayesian Non-Linear Models Bayesian Copula Models MODEL SELECTION AND AVERAGING Model Selection Strategies Bayesian Model Averaging Case Study: When to Select and When to Average Your Models Appendix Appendix W: Introduction to WinBUGS Appendix R: Introduction to R Appendix C: Code
