Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
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Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 13 - Bayesian Computational Methods
Authors
Brian Hartman | University of Connecticut
brian.hartman@uconn.edu
Chapter Preview
Bayesian methods have grown rapidly in popularity because of their general applicability, structured and direct incorporation of expert opinion, and proper accounting of model and parameter uncertainty. This chapter outlines the basic process and describes the benefits and difficulties inherent in fitting Bayesian models.
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