EE 322 Probabilistic
Methods for Electrical Engineers
The course will cover
descriptions of discrete & continuous random variables (probability
mass function, cumulative distribution function & probability
density function); mean and variance computation; conditioning &
Bayes rule;
statistical independence; and joint, conditional and marginal pdf and
cdf. Bernoulli, Binomial, Geometric, Poisson, Uniform,
Exponential, Gaussian and other
distributions of interest to EE students will be discussed. Time
permitting, we will
briefly learn basic concepts of random processes (deterministic,
nondeterminisitc, stationarity, ergodicity); of correlation
functions & power spectral density (PSD) & of discrete Markov
chains. Monte Carlo sampling will also be introduced.
Textbook: Bertsekas
&
Tsitiklis, Introduction
to Probability, Athena Scientific, 2002
Topics:
- Introduction:
Chapter 1: 1.1-1.6
- Single Random Variable (Discrete
& Continuous): 2.1-2.4, 3.1-3.3
- Multiple Random Variables:
2.5-2.7, 3.4-3.6, 4.1-4.5
- Random Processes, Correlation
functions &
Power Spectral Density (brief introduction, from Cooper &
McGillem)
- Discrete Markov Chains:
Parts of Chap 6
- Monte Carlo Sampling (brief
introduction)
Disability Accomodation:
If you have a documented disability and anticipate needing
accommodations in this
course, please make arrangements to meet with me soon. You will need to
provide documentation of your disability to Disability Resources (DR)
office, located on the main floor of the Student Services Building,
Room 1076, 515-294-7220.
Detailed Syllabus and Course
Information
Sheet: will be posted
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