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Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T)

(25 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • Lecture 1 Probability Models and Axioms
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
2
  • Lecture 10 Continuous Bayes’ Rule; Derived Distributions
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3
  • Lecture 11 Derived Distributions; Convolution; Covariance and Correlation
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
4
  • Lecture 12 Iterated Expectations; Sum of a Random Number of Random Variables
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
5
  • Lecture 13 Bernoulli Process
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
6
  • Lecture 14 Poisson Process I
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
7
  • Lecture 15 Poisson Process II
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
8
  • Lecture 16 Markov Chains I
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
9
  • Lecture 17 Markov Chains II
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
10
  • Lecture 18 Markov Chains III
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
11
  • Lecture 19 Weak Law of Large Numbers
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
12
  • Lecture 2 Conditioning and Bayes’ Rule
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
13
  • Lecture 20 Central Limit Theorem
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
14
  • Lecture 21 Bayesian Statistical Inference I
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
15
  • Lecture 22 Bayesian Statistical Inference II
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
16
  • Lecture 23 Classical Statistical Inference I
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
17
  • Lecture 24 Classical Inference II
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
18
  • Lecture 25 Classical Inference III; Course Overview
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
19
  • Lecture 3 Independence
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
20
  • Lecture 4 Counting
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
21
  • Lecture 5 Discrete Random Variables; Probability Mass Functions; Expectations
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
22
  • Lecture 6 Discrete Random Variable Examples; Joint PMFs
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
23
  • Lecture 7 Multiple Discrete Random Variables Expectations, Conditioning, Independence
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
24
  • Lecture 8 Continuous Random Variables
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
25
  • Lecture 9 Multiple Continuous Random Variables
Probabilistic System Analysis and Applied Probability (Fall 2010) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences

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