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Probability Theory Review
        CS221: Introduction to Artificial Intelligence
                     Naran Bayanbat
                       10/14/2011


Slides used material from CME106 course reader and CS229 handouts
Topics
•   Axioms of Probability
•   Product and chain rules
•   Bayes Theorem
•   Random variables
•   PDFs and CDFs
•   Expected value and variance
Introduction
• Sample space     - set of all possible
  outcomes of a random experiment
  – Dice roll: {1, 2, 3, 4, 5, 6}
  – Coin toss: {Tails, Heads}
• Event space          - subsets of elements in a
  sample space
  – Dice roll: {1, 2, 3} or {2, 4, 6}
  – Coin toss: {Tails}
Introduction
Set operations
Conditional Probability




       A   B
Conditional Probability



  Ω    A   B
Conditional Probability
Conditional Probability
Conditional Probability
Conditional Probability




P(A, B)   0.005

P(B)      0.02

P(A|B)    0.25
Bayes Theorem
Bayes Theorem




 Posterior
Probability
              Likelihood                    Prior
                           Normalizing   Probability
                            Constant
Bayes Theorem
Random Variables

                    Do ya feel
                   lucky, punk?
Cumulative Distribution Functions
Probability Density Functions
Probability Density Functions
Probability Density Functions
Probability Density Functions


                f(X)



                                X
Probability Density Functions


                f(X)



                                X
Probability Density Functions


                f(x)



                                x


                  F(x)
                       1


                                    x
Probability Density Functions


                f(x)



                                x


                  F(x)
                       1


                                    x
Expectation
Expectation
Variance
Gaussian Distributions
Questions?

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Cs221 probability theory

Hinweis der Redaktion

  1. A in unobserved, but B is observed
  2. A in unobserved, but B is observed
  3. F(x) is monotonically non-decreasing
  4. PDF is also called probability mass function when applied to discrete random variables
  5. PDF is also called probability mass function when applied to discrete random variables
  6. PDF is also called probability mass function when applied to discrete random variables
  7. PDF is also called probability mass function when applied to discrete random variables
  8. PDF is also called probability mass function when applied to discrete random variables
  9. PDF is also called probability mass function when applied to discrete random variables
  10. PDF is also called probability mass function when applied to discrete random variables
  11. PDF is also called probability mass function when applied to discrete random variables
  12. PDF is also called probability mass function when applied to discrete random variables