EECE7346 (Probabilistic System Modeling and Analysis) course reviews and ratings at Northeastern (NEU). Compare professors with RateMyProfessor reviews.
Covers fundamentals of probabilistic system modeling, building toward techniques that allow analyzing complex stochastic systems in a tractable fashion. Modeling large and complex systems requires reasoning about probabilistic behavior at a large scale. Reviews classic topics like Markov chains, convergence to a steady state, renewal processes, renewal reward processes, the strong law of large numbers, and the elementary renewal theorem. Additional topics include the asymptotic behavior of probabilistic systems, including stochastic approximation/Robbins-Monro type algorithms, and ODE/fluid limits. Illustrates how these modeling techniques can be applied in modeling real systems and adaptive algorithms, including queueing systems, distributed systems, and online learning algorithms like stochastic gradient descent.
Data updated October 2026.