This course will introduce the basic foundational aspects of probability theory primarily to an electrical engineering audience. In communications, signal processing and networking applications, probability theory and models play a vital role in design and implementation. This course will prepare a student to take courses such as Digital/Wireless C...
This course will introduce the basic foundational aspects of probability theory primarily to an electrical engineering audience. In communications, signal processing and networking applications, probability theory and models play a vital role in design and implementation. This course will prepare a student to take courses such as Digital/Wireless C...
Experiments, Outcomes and Events
30mExamples: Experiments and sample spaces
33mOperations on Events
26mExamples: Sample spaces and events
39mSigma Fields and Probability
39mDiscrete Sample Spaces
23mUnion and Partition
27mExamples: Probability Calculation for Equally likely Outcomes
40mDefinition and Basic Properties
40mBayes' Rule for Partitions
10mExamples: Conditional probability
39mExample of Detection
39mExample: Coloured Cards from a Box
20mIndependence of Events
24mExamples: Independence
37mCombining Independent Experiments
15mConditional Independence
21mExamples and Computations with Conditional Independence
13mBinomial and Geometric Models
21mExamples: Binomial and Geometric Model
26mDefinition and Discrete Setting
28mRandomVariables and Events
17mExamples: Discrete random variables
17mImportant distributions
17mExamples: Discrete PMFs
17mReal-life modeling example
17mMore Distributions
16mConditional PMFs, Conditioning on an event, Indicator random variables
30mExample: Conditioning on an event, Indicator random variables
30mMultiple random variables and joint distribution
20mExample: Two random variables
24mMarginal PMF
17mTrinomial joint PMF
28mEvents and Conditioning with Two Random Variables
24mIndependent random variables
16mMore on independence
32mExample: IID Repetitions
25mAddition of Random Variables
32mSum, Difference and Max of Two Random Variables
27mMore Computations: Min of Two Random Variables
14mExample: X Y, X-Y, min(X,Y), max(X,Y)
30mReal line as sample space
30mProbability density function (pdf)
19mCumulative distribution function (CDF)
29mContinuous random variables
15mpdf and CDF of continuous random variables
15mSpinning pointer example
15mImportant continuous distributions
15mMore continuous distributions
20mTwo-dimensional real sample space
19mJoint pdf and joint CDF
19mMore on assigning probability to regions of x-y plain
20mDarts example and marginal pdfs
29mIndependence to two continuous random variables
29mExamples: two independent continuous random variables
15mProb computation of probability of a non-rectangular region
14mTransformations of random variables
19mCDF method
15mpdf method
24mExamples
23mOne-to-one transformations
15mExpected Value or Mean of a Random Variable
24mProperties of Expectation
13mExpectation Computations for Important Distributions
32mVariance
32mExamples of Variance
19mExpectations with Two Random Variables
14mCorrelation and Covariance
14mExamples: Continuous Distributions
30mExamples: Symmetry
13mLive Session
1h 64 min