Description: In this paper we propose a model that can be used to analyze correlated claims data. In the process we introduce a class of multivariate generalized Poisson distributions. Then we present posterior distributions of its parameters. These distributions are difficult to manipulate, so we employ Markov chain Monte Carlo methods to draw random numbers from posteriors and predictive distributions. We present a collection of EXCEL VBA functions and subroutines to perform the simulations.

Description: Premium computation in a Bayesian context requires the use of a prior distribution [structure function] that the risk parameter follows in the heterogeneous portfolio. This paper contributes to the analysis of credibility theory by identifying the unique relationship between the prior distribution and credibility formula. The latter corresponds to a suggestive form of expressing the premium to be charged to a policyholder as a weighted sum of the sample mean and collective premium. Results for net premium principle and Poisson and negative binomial likelihood functions are shown.

Description: In this Chapter we present a novel way for estimating aggregated EC figures based on Bayesian copula estimation. Contrary to the classic approach of using a single inter-risk- correlation matrix we derive a probability distribution of possible correlation matrices that enables us to tackle the important issue of parameter uncertainty. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: An in-depth look at how actuarial skills and competencies picked up in the field can be applied to topics that aren't traditionally actuarial - with this article focusing on card counting in the casino game blackjack.

Description: This is a presentation from the Actuarial Research Conference in August 2006 in Montreal. It is on Bayesian Inference Resistant to Outliers. Presented by Alain Desgagne.

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.

Description: Book review of The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines and Emerged Triumphant from Two Centuries of Controversy by Sharon Bertsch McGrayne.

Description: Poisson regression models are commonly used in actuarial science to model count data e.g., the number of claims and covariates. The standard parametric formulation assumes that the response to covariates is constant across the population under study. Additionally, the shape of the posterior distribution is rather inflexible. In practice, however, the response to covariates may depend on a latent class and the posterior distribution may be highly nonparametric. This paper proposes a Bayesian nonparametric model in which a Dirichlet process prior on the regression coefficients leads to a clustering of observations into groups having the same response to covariates. This paper illustrates the utility of this approach with an example dataset of diabetes deaths.

Description: This paper offers a decision making procedure for solving complex problems step by step. It presents the decision analysis process for both public and private decision making, using different decision criteria, different types of information, and information of varying quality.

Description: Feature article discussing the use of Bayesian Networks as predictive modeling tools.