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Essentials of Stochastic Processes (1)

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February 26, 2019
26

Essentials of Stochastic Processes (1)

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February 26, 2019
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  1. Essentials of Stochastic Processes (1) Prologue : Review of probability

    B3 English Seminar 2019/2/26 Nagaoka University of Technology Atom Yoshizawa
  2. References Author : Rick Durrett Translators : Norio Konno et

    al. “Essentials of Stochastic Process” , Maruzen Publishing Ltd. (2012) 2
  3. Contents 0.1 Probabilities, Independence 0.2 Random Variables, Distributions 0.3 Expected

    Value, Moments 3
  4. 0.1 Probability and independence If 1 , ⋯ , are

    events , ・pairwise independent : ∩ = ( ) for each ≠ ・independent : 1 ∩ ⋯ ∩ = 1 ⋯ ( ) for any 1 ≤ 1 ≤ ⋯ ≤ ≤ Incidentally, if is also an event , ・Bayes’ formula : = (∩) ∑ (∩) = ()(|) ∑ ()(|) 4
  5. 0.1 Probability and independence Ex) Flip three coins. Event :

    the first and second coins are in the same direction Event : the second and third coins are in the same direction Event : the third and first coins are in the same direction = = = 2 4 = 1 2 5
  6. 0.1 Probability and independence The front and back of the

    coin are denoted by , . ∩ = ∩ = ∩ = , ∩ = 2 8 = 1 4 = 1 2 � 1 2 = i.e. and are independent. Similarly and are independent, and are also independent. hence, , and are pairwise independent . 6
  7. 0.1 Probability and independence However, the three events , and

    are not independent . ∩ ∩ = , ∩ ∩ = 2 8 = 1 4 ≠ 1 2 3 = 7 If events are pairwise independent, they are not always independent.
  8. 0.2 Stochastic variable, distribution Various probability distributions ・binomial distribution ・geometric

    distribution ・Poisson distribution ・uniform distribution ・exponential distribution ・standard normal distribution 8
  9. 0.2 Stochastic variable, distribution ・binomial distribution : number of successes,

    : probability to succeed = = (1 − )− for = 0, ⋯ , ・Poisson distribution : stochastic variable, : parameter = = − for = 0,1,2, ⋯ 9
  10. : discrete distribution The expected value of ℎ() is defined

    by the following equation 𝐸 = � ℎ ( = ) 𝐸𝐸 : the expected value of ℎ = : the expected value of ℎ = (-th order moment) 10 0.3 Expected value, moment