Lehigh’s Probabilistic Modeling Group (PMG) operates successfully with the contribution of graduate students, postdoctoral research fellows, as well as undergraduate students. The faculty members of the group offer several courses in probabilistic analysis, covering both its theory and applications. Please notice that the following list is partial and we are working to complete it.

MATH 309 - Theory of Probability

Probability courses teach students how to think systematically and mathematically about uncertainty and randomness. Students learn that rigorous and logical reasoning is essential to read, model and analyze stochastic problems.Read more

STAT 410/MATH 310 - Random Processes and Applications

STAT 410/MATH 310 mixes discrete and continuous mathematics. A key concept in probability is conditioning - how partial knowledge changes how one thinks about a problem. The technique of conditioning will be used throughout the course to solve interesting problems in probability and expectation. Read more

MATH 463 - Advanced Probability

MATH 463 provides a comprehensive treatment of the most common tools in Probability Theory, using a measure theoretic approach. Read more

MATH 464 - Advanced Stochastic Processes

MATH 464 provides a rigorous coverage of important aspects of the theory of stochastic processes, based on measure-theory. Read more

BioE/ChE 497 - Stochastic Processes: Theory and Applications in Biology

BioE/ChE 497 Stochastic, i.e. "noisy", contributions are unavoidable in Nature. In the particular case of biological systems, the inherent randomness of biochemical reactions, the fluctuation of external factors, the variability among cells, the low copy number of constituents, etcetera are sources of uncertainty that condition the cellular processes. Importantly, noise is not always a nuisance that challenges the reliability of the biological functions but, in many cases, plays a fundamental and constructive role in Biology. Read more

CEE 406 - Reliability of Structural Components and Systems

CEE 406 aims to present a unified view of the techniques and theories for the analysis of structural reliability of components and systems. Read more

CEE 431 - Life-Cycle of Structural Systems

CEE 431 is designed to review and develop the principles and methods of assessing the life-cycle performance of new and existing structural systems, designing structures for lifetime performance, and optimizing the remaining life of existing structures, considering uncertainties in structural performance, demands placed on structural systems, structural maintenance and monitoring, and costs. Read more

CEE 358/458 - Random Vibrations

CEE 358/458 covers the theory of random functions (stochastic processes and random fields) and their application to engineering problems. Read more

ISE 416 - Dynamic Programming

ISE 416 Stochastic Dynamic Programming, or just Dynamic Programming, is the principle governing how optimal sequential decisions are made in the face of uncertainty. Read more

ISE 429 - Stochastic Models and Applications

ISE 429 is an introduction to stochastic processes modeling, analysis techniques and applications. Read more