Applications of Probability Theory to Actuarial Science

Catalog description

This course introduces students to actuarial science topics and the actuarial profession. To become an actuary, individuals must pass a series of professional examinations that accredit them as professionals in the field. This course provides an introduction to the material on the earlier exams such as applications of probability theory to insurance, financial mathematics (compound interest and annuities), and provides instruction on spreadsheets, so that students can perform their homework using them. Topics covered include applications of the following to insurance and actuarial science: conditional probability, independence, combinatorial principles, Bayes Theorem, and random variables. Specific probability distributions used include the binomial, uniform, Poisson, geometric, negative binomial, hyper-geometric, and multinomial discrete distributions, as well as the exponential, normal, uniform, and gamma continuous distributions. Expectations, distribution parameters, means, medians, modes, variances, skewness, and moment generating functions are also covered. The more advanced topics of joint, marginal, and conditional distributions are used, along with functions and transformations of random variables. The application of probability theory to risk management is addressed. Throughout the course, sample problems will be reviewed to help prepare students for the actuarial professional exams.