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Stochastic ProcessesStochastic Processes
Random Walks
Hamid R. Rabiee
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Overview R di A i t Reading Assignment Chapter 10 of textbook
Further Resources MIT Open Course Ware S. Karlin and H. M. Taylor, A First Course in Stochastic
Processes, 2nd ed., Academic Press, New York, 1975.Processes, 2nd ed., Academic Press, New York, 1975.
2Stochastic Processes
Antony Gormley's Quantum CloudSculpture in London was Sculpture in London was designed by a computer using a random walk algorithmalgorithm
3Stochastic Processes
Outline B i D fi iti Basic Definitions Mathematical Definition Probability of return First return Probability of Eventual Return Application: Satisfiability Problem Application: Satisfiability Problem
4Stochastic Processes
Basic Definitions I RW d l ti l t k it t In a RW model, a particle takes a unit step up or
down at regular intervals, and represents the position of particle at nth step
nSp p p
Examples: The path traced by a molecule The path of a drunk man Gambler’s wealth
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Mathematical Definition C id f i i d d Consider a sequence of i.i.d. random
variables taking the values +1, -1 with probabilities
1 2, ,..., ,...nx x x,p q1 with probabilities
denotes the partial sum of n RVs :,p q
ns
The RW is symmetric if
1 2 ... ...n ns x x x 0 0S
0 5p q The RW is symmetric if 0.5p q
{ } , 2k n kn
nP s r p q r k n
k
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k
Probability of returnW th ti l t d t i i We say the particle returned to origin
at time t if X(t) = 0. F h i l i i i For the particle to return to origin, it
must do m up moves and m down moves.
Lemma: the Probability of returning to i i t dd ti i 0origin at an odd time is 0
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Probability of return (cont.)
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First return
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First return (Cont.)
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First Return Solution
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First Return Solution (Cont.)
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Probability of Eventual Return
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Prob. of Eventual Return (Cont.)
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Application: Satisfiability Problem P bl D fi iti Problem Definition:
Consider n Boolean variables A clause is combination of subset of variables or negation of
1 2, ,..., nx x xg
variables with disjunction (Suppose in this problem each clause has exact two variables):
We have a formula consist of conjunction of clauses. the i jx x
satisfiability problem is either to determine values for the variables that result in the formula being TRUE, or to determine that the formula is never true
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Application: Satisfiability Problem P b bili ti S l ti Probabilistic Solution:
1. Consider an initial TRUE-False random assignment to each variable.
2. If formula isn’t satisfied, consider a clause that is not satisfied and randomly choose one of the Boolean variables in that clause and change its valueg
3. If this new setting makes the formula TRUE then stop, otherwise repeat step 2. If you have not stopped after repetitions, then declare that the 2 (1 4 .75)n p ,formula cannot be satisfied
( )
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Application: Satisfiability Problem S l ti A l i Solution Analysis: Assuming that there is a satisfiable
assignment of truth values and let A be such assignment of truth values and let A be such an assignment
Let denote the number of the n variables jSwhose values at the jth stage of the algorithm agree with their values in A
Thus and with probability at most
j
1S S Thus and with probability at most ½ and with probability at least ½.
1 1j jS S 1 1j jS S
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Application: Satisfiability Problem S l ti A l i (C t ) Solution Analysis (Cont.): Thus we have a random walk Compute probability of reaching from to0S Compute probability of reaching from to
in at most steps Compute probability that we wrongly declare
0 0S
kS n 2 (1 4 .75)n
Compute probability that we wrongly declare that the formula cannot be satisfied
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