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{{Use dmy dates|date=October 2017}}
{{Mcn|date=May 2025}}
A '''maximal entropy random walk''' ('''MERW''') is a popular type of [[biased random walk on a graph]], in which transition probabilities are chosen accordingly to the [[principle of maximum entropy]], which says that the [[probability distribution]] which best represents the current state of knowledge is the one with largest entropy. While a standard [[random walk]] samples for every vertex a uniform probability distribution of outgoing edges, locally maximizing [[entropy rate]], MERW maximizes it globally (average [[entropy production]]) by sampling a uniform probability distribution among all paths in a given graph.
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== Examples ==
[[Image:Examples of using MERW, Fibonacci coding(left) and 1D defected lattice (right).png|thumb|320px|right|Left: choosing the optimal probability after symbol 0 in [[Fibonacci coding]]. Right: a one-dimensional defected lattice and its stationary density for
Let us first look at a simple nontrivial situation: Fibonacci coding, where we want to transmit a message as a sequence of 0s and 1s, but not using two successive 1s: after a 1 there has to be a 0. To maximize the amount of information transmitted in such sequence, we should assume a uniform probability distribution in the space of all possible sequences fulfilling this constraint.
A more complex
<math>(\lambda \psi)_x = (A\psi)_x = \psi_{x-1}+ (1-V_x) \psi_x +\psi_{x+1}</math>
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<math>E \psi_x =-(\psi_{x-1} -2 \psi_x +\psi_{x+1}) + V_x \psi_x</math>
where <math>E = 3-\lambda</math> is minimized now, becoming the analog of energy. The formula inside the bracket is [[discrete Laplace operator]], making this equation a discrete analogue of the stationary [[Schrödinger equation]]. As in quantum mechanics, MERWs predict that the probability distribution
==See also==
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