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  2. Hidden Markov model - Wikipedia

    en.wikipedia.org/wiki/Hidden_Markov_model

    Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]

  3. Examples of Markov chains - Wikipedia

    en.wikipedia.org/wiki/Examples_of_Markov_chains

    A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain, indeed, an absorbing Markov chain. This is in contrast to card games such as blackjack, where the cards represent a 'memory' of the past moves. To see the difference, consider the probability for a certain event in the game.

  4. Markov model - Wikipedia

    en.wikipedia.org/wiki/Markov_model

    For example, a series of simple observations, such as a person's location in a room, can be interpreted to determine more complex information, such as in what task or activity the person is performing. Two kinds of Hierarchical Markov Models are the Hierarchical hidden Markov model [2] and the Abstract Hidden Markov Model. [3]

  5. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    D. G. Champernowne built a Markov chain model of the distribution of income in 1953. [86] Herbert A. Simon and co-author Charles Bonini used a Markov chain model to derive a stationary Yule distribution of firm sizes. [87] Louis Bachelier was the first to observe that stock prices followed a random walk. [88]

  6. Category:Markov models - Wikipedia

    en.wikipedia.org/wiki/Category:Markov_models

    Markov chain; Markov chain central limit theorem; Markov chain geostatistics; Markov chain Monte Carlo; Markov partition; Markov property; Markov switching multifractal; Markovian discrimination; Maximum-entropy Markov model; MegaHAL; Models of DNA evolution; MRF optimization via dual decomposition; Multiple sequence alignment

  7. Forward algorithm - Wikipedia

    en.wikipedia.org/wiki/Forward_algorithm

    The Forward algorithm will then tell us about the probability of data with respect to what is expected from our model. One of the applications can be in the domain of Finance, where it can help decide on when to buy or sell tangible assets. It can have applications in all fields where we apply Hidden Markov Models.

  8. ‘Fleeing into the Unkown’ by Huffington Post

    testkitchen.huffingtonpost.com/Fleeing-into-the...

    Many of them put their fate in the hands of human smugglers and travel thousands of miles in the hope of finding a better life. These men, women and children make up just some of the over one million migrants and refugees who have sought asylum in Europe this past year.

  9. Mean-field particle methods - Wikipedia

    en.wikipedia.org/wiki/Mean-field_particle_methods

    If () = is the unit function and =, the interaction between the particle vanishes and the particle model reduces to a sequence of independent copies of the Markov chain . When ϵ = 0 {\displaystyle \epsilon =0} the mean field particle model described above reduces to a simple mutation-selection genetic algorithm with fitness function G and ...