Description: This abstract describes a paper that assesses the optimal manner to collapse a factor with many levels into one with a smaller number of levels.

Description: A method is presented which uses Bayes Lemma in an elementary way to construct posterior probabilities to be attached to each of several alternative prior distributions, given a few observations. The availability of efficient computer programs to numerically perform discrete convolutions for producing prior distributions makes this a practical method. From the Actuarial Research Clearing House 1978, Vol.2.

Description: This presentation considers the impact of sampling variation on the calibration of stochastic mortality models.

Description: This paper attempts a model for a mix of DB and DC retirement plans that meets the dual objectives of predictability of employer cash contributions, and sufficient employee retirement benefit. From Current Pension Actuarial Practice in Light of Financial Economics Symposium, M-RS04-1.

Description: This teaching session is about conveying to users of models and forecasts how uncertain the outcome of stochastic projections really is.

Description: The objective of this paper is to present a technical exposition of the rudiments of Bayesian statistics.

Description: Exam study notes on credibility theory.

Description: In this paper, the Bayesian model of collective risk theory is extended in the sense that a large class of distributions is used instead of a single one. The authors then investigate the sensitivity of variance premium principle when the structure function belongs to that class. According to robust Bayesian methodology, the uncertainty in the prior can be modeled by specifying a class of priors instead of a single one. The authors examine the ranges of the Bayesian premium, also called experience rated premium, when the priors belong to that class. Most of the robust Bayesian procedures developed measures of sensitivity of quantities which can be expressed in terms of posterior expectation [e.g., mean, variance and probability of given sets]. Nevertheless, relatively few papers have been related to measure Bayesian sensitivity of quantities which can be expressed in terms of ratio of two posterior expectations, as occurs in the variance premium principle. Appropriate techniques to solve this situation are considered. Unimodality turns out to be very convenient for modelling subjective beliefs about the risk parameter. The very common Poisson-Gamma model is developed in depth using this paper's methodology.

Description: This paper discusses the Bayesian graduation method of Kimeldorf and Jones and develops methods for managing four technical problems in their method. It also includes a discussion of the paper. From Transactions of Society of Actuaries 1977, Vol. 29.

Description: This paper examines some new Bayesian models for loss reserving inspired by a consideration of some of the methods and techniques appearing in the traditional chain ladder literature. This includes a possibly order restricted hierarchical Bayesian model for the year over year development factors. The issues of reserve variability and ranges of reasonable reserve estimates are considered. A new Bornhuetter-Ferguson styled Bayesian method of estimate revision is also discussed. These Bayesian models can be implemented using Markov chain Monte Carlo MCMC methods in a variety of programming languages, and can serve as starting points for more advanced models. This paper describes their implementation in WinBUGS a specialized software program for MCMC simulations. Illustrative WinBUGS code is included.