Description: Appendix to the Critical Review of Stochastic Simulation Literature and Applications for Health Actuaries. The Development of a Simulation Model of Primary Prevention Strategies for Coronary Heart Disease

Description: A study on stochastic simulation literature as it applies to a health actuary.

Description: The game of baseball has characteristics that suggest its analysis as a Markov chain.

Description: An introduction to Hidden Markov Models, which are used extensively in speech and facial recognition technology. They are a useful tool in forecasting time series and in developing categorizations of data based on the values of the data themselves.

Description: A study of disability experience examines cost burden and degree of disability, and how these measures vary across different subpopulations. This article summarizes a presentation given at the Society of Actuaries’ Living to 100: Survival to Advanced Ages International Symposium held on Jan. 7–9, 2008 in Orlando, Florida. An abstract of the author's paper was presented in the September 2008 issue of Long-Term Care News.

Description: This paper develops a general approach to actuarial calculations in applications that can be modeled as multi-state processes. Such situations arise when benefits are payable upon a change in the status of the insured or while the insured maintains a given status. From the Transactions of Society of Actuaries 1994, Vol. 46.

Description: In this continuation of our July 2013 article we explore an algorithm for evaluating Hidden Markov Models in detail. A preferred method in speech recognition technology, Hidden Markov Models are a promising technique when dealing with data you suspect may be driven by a change in a unobservable state. The example in the article is provided in an Excel workbook for the reader to follow along with.

Description: This paper shows the application of a threshold strategy on the surplus level of an insurance risk model results in a process that can be analyzed as a doubly infinite Markov chain with finite blocks. The resulting procedures are then used to calculate the probability distributions for time of ruin, the surplus immediately prior to ruin, and the deficit at ruin. From the Actuarial Research Clearing House, 2008, Vol.1.