## Introduction to Data Communication Networks

###### These videos provide a short overview of fundamentals of probability theory.  We review events, conditional probability, probability density functions (PDFs), cumulative distribution functions (CDFs), mean, variance, and moments.  These probability and random variable concepts will be used in the remainder of the course.

Introduction

10/12/13

Running Time: 2:43

Fundamentals of probability theory introduction for an undergraduate course in digital communications

Events

10/13/13

Running Time: 27:59

Explores basic quantities related to events: sample space, event probability, Bayes' Theorem, Total Probability Theorem, Mutually Exclusive events, Independent Events, Bernoulli Trials, etc.

Conditional Probability Example

10/14/13

Running Time: 3:13

This problem demonstrates conditional probabilities through a simple computational example. The probability of drawing two specific cards from a deck of cards without replacement is computed.

Binary Symmetric Channel

10/16/13

Running Time: 6:31

This example defines and investigates a communication channel called the binary symmetric channel (BSC).

Repeat Coding

10/17/13

Running Time: 12:19

This example investigates repeat coding, an error control coding scheme that improves transmission reliability at the expense of reduced data rates.

CDFs and PDFs

10/22/13

Running Time: 11:06

Gives an overview of two important functions for describing random variables, the cumulative distribution function (CDF) and probability density function (PDF).

Gaussian Random Variables

10/26/13

Running Time: 9:53

Examines the PDF and CDF of Gaussian random variables.  Also, discusses other functions related to Gaussian random variables such as the Q function, error function, and complementary error function.

Multiple Random Variables

10/26/13

Running Time: 8:29

PDF and CDF concepts for a single random variable are extended to multiple random variables.  This yields new quantities such as joint distributions, conditional distributions, and independent random variables.

Mean, Variance, and Moments

10/26/13

Running Time: 9:11

Defines the mean, moment, variance, and central moment of a random variable.  All of these quantities are computed using a weighted average of the random variable probability density function.

Gaussian PDF Example

10/28/13

Running Time: 6:56

Examples performs several computations with a Gaussian random variable.  Specifically, the Q-Function is used to compute the probability of the Gaussian random variable being less than or greater than a number.

Mean and Variance Example Computations

11/15/13

Running Time: 7:23

This example investigates a continuous random variable X with associated probability density function p(x).  The mean, mean-squared, and variance of the random variable are computed from the definition (i.e. taking the appropriate weighted average of the PDF).