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  2. Principle of maximum caliber - Wikipedia

    en.wikipedia.org/wiki/Principle_of_maximum_caliber

    The principle of maximum caliber (MaxCal) or maximum path entropy principle, suggested by E. T. Jaynes, [1] can be considered as a generalization of the principle of maximum entropy. It postulates that the most unbiased probability distribution of paths is the one that maximizes their Shannon entropy. This entropy of paths is sometimes called ...

  3. Maximal entropy random walk - Wikipedia

    en.wikipedia.org/wiki/Maximal_Entropy_Random_Walk

    To practically use such long sequences, after 1 we have to use 0, but there remains a freedom of choosing the probability of 0 after 0. Let us denote this probability by , then entropy coding would allow encoding a message using this chosen probability distribution. The stationary probability distribution of symbols for a given turns out to be

  4. Maximum-entropy random graph model - Wikipedia

    en.wikipedia.org/wiki/Maximum-entropy_random...

    Any random graph model (at a fixed set of parameter values) results in a probability distribution on graphs, and those that are maximum entropy within the considered class of distributions have the special property of being maximally unbiased null models for network inference [2] (e.g. biological network inference).

  5. Convergence of random variables - Wikipedia

    en.wikipedia.org/wiki/Convergence_of_random...

    However, for a given sequence {X n} which converges in distribution to X 0 it is always possible to find a new probability space (Ω, F, P) and random variables {Y n, n = 0, 1, ...} defined on it such that Y n is equal in distribution to X n for each n0, and Y n converges to Y 0 almost surely. [11] [12] If for all ε > 0,

  6. Watts–Strogatz model - Wikipedia

    en.wikipedia.org/wiki/Watts–Strogatz_model

    Watts–Strogatz small-world model generated by igraph and visualized by Cytoscape 2.5. 100 nodes. The Watts–Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.

  7. Percolation threshold - Wikipedia

    en.wikipedia.org/wiki/Percolation_threshold

    Maximum entropy; Soft configuration ... and ask for the probability P that there is a path from the top boundary to the bottom boundary. ... (g) 3,6,10 5 1 ⁄ 2: 0 ...

  8. Mean free path - Wikipedia

    en.wikipedia.org/wiki/Mean_free_path

    where ℓ is the mean free path, n is the number of target ... note that the probability that a particle is absorbed ... 10 16 – 10 13: 10 22 – 10 19: 0.1 – 100 ...

  9. Longest path problem - Wikipedia

    en.wikipedia.org/wiki/Longest_path_problem

    In graph theory and theoretical computer science, the longest path problem is the problem of finding a simple path of maximum length in a given graph. A path is called simple if it does not have any repeated vertices ; the length of a path may either be measured by its number of edges, or (in weighted graphs ) by the sum of the weights of its ...

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