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: This paper proposes a more generalized Gerber-Shiu G-S function which contains both the G_S expected discounted penalty function and dividends paid up to ruin, as well as many others that have not been taken into consideration in the same systematic way, such as the insurer's accumulative utility, expected total discounted claim expenses, etc. The major result provides a unifying approach for all ruin-related quantities that fall in the category of the generalized GS function. From the Actuarial Research Clearing House, 2008, Vol. 1.

Description: This paper describes risk-factor-dependent multiple decrements and actuarial cost functions as a general compartment model, a discrete time and discrete state stochastic model of morbidity and mortality processes. For large numbers of risk factors, the model is extended to its natural continuous time and state form. From Transactions of Society of Actuaries 1991, Vol. 43.

Description: This paper presents diagnostics and tests for tile retrospective identification of abrupt change in the mean level of a process where the mean and variance of the underlying random variables cannot be functionally separated. From Actuarial Research Clearing House 1994, VOL. 1.

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: This paper considers the Bayesian analysis of the generalized Poisson distribution GPD and the generalized Poisson regression GPR model. Markov chain Monte Carlo MCMC methods and random variety generation strategies such as adaptive rejection sampling ARS for log-concave densities and adaptive rejection Metropolis sampling ARMS are discussed and then utilized in order to advance the Bayesian analysis of these generalized Poisson models.

Description: This paper endeavors to obtain the explicit formulas of the probability of ruin in a Markov-modulated model where claim intensities, claim sizes and premiums vary according to a Markovian environment.

Description: This paper builds a new risk model for a firm which is sensitive to its credit quality. A modified Jarrow, Lando and Turnball model [Markov Chain model] is used to model the credit rating. Recursive equations for finite time ruin probability and distribution of ruin time are derived. Coupled Volterra type integral equation systems for ultimate ruin probability, severity of ruin and joint distribution of surplus before and after ruin are also obtained. Some numerical results are included.

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.