NOTE: Information on Dr. Guikema's University of Michigan classes will be added once it is available. The information below is about courses taught previously at Johns Hopkins University.

Dr. Guikema taught five courses at Johns Hopkins University:

Fall semesters, yearly. This course introduces the methods of probabilistic risk and decision analysis. Topics will include risks in daily life, public attitudes towards risk, fault trees, event trees, decision trees, utility functions, risk attitude, and value of information calculations.

Prereq: Introductory Statistics
Spring semesters in the past, but it has now moved the fall semester. This course assumes that students have a solid grounding in basic statistics, including linear regression, ANOVA, and similar methods. It covers a range of semi-parametric, non-linear, and data mining techniques. The focus on establishing a rigorous, theory-grounded approach for data analysis, especially for developing strong predictive models. Students complete an in-depth project in which they analyze a data set, preferably from their own research. A number of these papers have been published as peer-reviewed journal articles. The course uses the R statistical language.

Prereqs: EN.550.420 AND EN.550.430 or equivalent.
Taught Spring 2010, 2011, 2013. Likely not taught again until spring 2015. This course is a mix of seminar-style guided discussions and student presentations and lectures on specific topics. It gives an overview of the infrastructure systems that form the basis for health, security, and economic prosperity in the developed world and give an overview of some of the most pressing infrastructure challenges in the developing world. The focus is on quantitative modeling of infrastructure performance, sustainability, and resilience for supporting infrastructure management and policy decision-making.
First taught Spring 2011. This course provides an introduction to stochastic simulation and game theory. It covers a mix of the theoretical background and the practical use of these two methods. The stochastic simulation portion of the covers both discrete even and time step methods. It also covers random number generators, analysis of output, comparison of systems, variance reduction techniques, and linkages between simulation and optimization. The game theory portion of the course provides an introduction to the basic types of games: static games of complete information, dynamic games of complete information, static games of incomplete information, and dynamic games of incomplete information. Several case studies are covered. NOTE: This course will not likely be offered again.
This course was first taught in Spring 2013. This course is intended for Ph.D. students and potentially others doing research in risk analysis, decision analysis, and related areas. We will read and discuss the foundational literature of these fields. This includes foundational literature in Bayesian probability theory, utility theory, and decision analysis.