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

(25 Lectures Available)

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

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