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Engineering Probability
Fall 2021

Lecture 1

Experiments, sample space, events

Lecture 2

Axioms, probabilistic models, counting methods

Lecture 3

Conditional probability

Lecture 4

Baye's rule, independence, Bernoulli trials​

Lecture 5

Discrete random variables


Lecture 6

Expected value and moments 

Lecture 7

Conditional probability mass functions

Lecture 8

Cumulative distribution functions

Lecture 9

Probability distribution functions, continuous random variables

Lecture 10

The Gaussian random variable, Q function 

Lecture 11

Expectations of a random variable 

Lecture 12

Functions of a random variable; inequalities

Lecture 13

Two random variables (discrete)

Lecture 14

Two random variables (continuous); independence

Lecture 15

Joint expectations, correlation, covariance

Lecture 16

Conditional PDFs; Bayesian and maximum likelihood estimation

Lecture 17

Conditional expectations

Lecture 18

Sums of random variables

Lecture 19

Sums of random variables

Lecture 20

Central limit theorem; confidence intervals

Lecture 21

MAP, ML, and MMSE estimation

Lecture 22

Hypothesis testing

Lecture 23

Testing the fitness of a distribution; generating random samples

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