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Unformatted text preview: Stat 150 Stochastic Processes Spring 2009 Lecture 18: Markov Chains: Examples Lecturer: Jim Pitman A nice collection of random walks on graphs is derived from random movement of a chess piece on a chess board. The state space of each walk is the set of 8 8 = 64 squares on the board: a b c d e f g h 1 2 3 4 5 6 7 8 Each kind of chess piece at a state i on an otherwise empty chess board has some set of all states j to which it can allowably move. These states j are the neighbours of i in a graph whose vertices are the 64 squares of the board. Ignoring pawns, for each of king, queen, rook, bishop and knight, if j can be reached in one step from i , this move can be reversed to reach i from j . Note the pattern of black and white squares, which is important in the following discussion. The King interior corner edge 8 possible moves 3 possible moves 5 possible moves 1 Lecture 18: Markov Chains: Examples 2 In the graph, i j means that j is a kings move from i , and i is a kings move from j . Observe that N ( i ) := #( possible moves from i ) { 3 , 5 , 8 } . From general discussion of random walk on a graph in previous lecture, the reversible equilibrium distribution is i = N ( i ) where := X j N ( j ) = (6 6) 8 + (4 6) 5 + 4 3 Question : Is this walk regular? Is it true that m : P m ( i,j ) > 0 for all i,j ?...
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This note was uploaded on 10/02/2009 for the course STAT 87528 taught by Professor Pitman,jim during the Spring '09 term at University of California, Berkeley.
 Spring '09
 Pitman,Jim
 Markov Chains

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