Course Material  Resources Home
Instructor: Prof. K. Chandra
Office: Falmouth 203 (North Campus)
Tel: 978 934 3356
Email: kavitha_chandra@uml.edu

Course Syllabus (pdf)


Class Notes; A first course in stochastic models, by H.C. Tijms, Wiley 2003; Lessons in Estimation Theory for Signal Processing, Communications, and Control, by J.M. Mendel, Prentice Hall, 1995

Meets: Mondays, 6:8:50 pm, Fall 2015


Applied stochastic estimation introduces methods for estimating parameters and states of systems characterized by random features. Problems of queueing, resource allocation, signal estimation, prediction and forecasting are considered. Statistical metrics for estimation and hypothesis are presented. Methods for simulating queues and other dynamical systems are discussed and implemented in assigned projects.


Background:  Probability and Random Processes 16.584  or equivalent. Programming skills: R/Python/MATLAB/C/Fortran

University of Massachusetts 
Department of Electrical & Computer Engineering
Center for Advanced Computation and Telecommunications