Transcription of EE126: Probability and Random Processes - Lecture 1 ...
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OutlineLogisticsIntroductionModelExample sEE126: Probability and Random ProcessesLecture 1: Probability ModelsAbhay ParekhUC BerkeleyJanuary 18, 2011 OutlineLogisticsIntroductionModelExample s1 Logistics2 Introduction3 Model4 ExamplesOutlineLogisticsIntroductionMode lExamplesWhat is this course about?Most real-world problems involve uncertainty1 Predictions:Will you like this class? Will you get an A ?Will the Giants win the world series next year? What are theodds?2 Strategy/Decision MakingHow should you bet in blackjack?Should you buy shares of AAPL?
probability 4 The probabilities summed over all of the base outcomes always equals 1 Example: Toss a fair coin twice 1 Base outcomes are HH,HT,TH,TT 2 This covers all the possibilities. M.E. 3 Each of these outcomes is equally likely 4 Assign each outcome a probability of 0:25. The list (set) of base outcomes is called the Sample Space.
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PROBABILITY AND RANDOM PROCESSES, Processes, Probability, Statistics, and Random Processes for Electrical Engineering, Probability, Statistics, and Random Processes, Probability, Random, Random Processes, Probability, Statistics, and Stochastic Processes, PROBABILITY AND RANDOM PROCESSES FOR ELECTRICAL AND COMPUTER ENGINEERS, Probability Random Variables and Stochastic Processes, Stochastic Processes, Stochastic, Ch 4 Solutions, Leon-Garcia INSTRUCTOR’S SOLUTIONS MANUAL