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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. Viterbi algorithm - Wikipedia

    en.wikipedia.org/wiki/Viterbi_algorithm

    Tutorial on convolutional coding with viterbi decoding, by Chip Fleming; A tutorial for a Hidden Markov Model toolkit (implemented in C) that contains a description of the Viterbi algorithm; Viterbi algorithm by Dr. Andrew J. Viterbi (scholarpedia.org).

  4. Baum–Welch algorithm - Wikipedia

    en.wikipedia.org/wiki/Baum–Welch_algorithm

    A hidden Markov model describes the joint probability of a collection of "hidden" and observed discrete random variables.It relies on the assumption that the i-th hidden variable given the (i − 1)-th hidden variable is independent of previous hidden variables, and the current observation variables depend only on the current hidden state.

  5. Markov model - Wikipedia

    en.wikipedia.org/wiki/Markov_model

    A hidden Markov model is a Markov chain for which the state is only partially observable or noisily observable. In other words, observations are related to the state of the system, but they are typically insufficient to precisely determine the state. Several well-known algorithms for hidden Markov models exist.

  6. Hidden Markov random field - Wikipedia

    en.wikipedia.org/wiki/Hidden_Markov_random_field

    In statistics, a hidden Markov random field is a generalization of a hidden Markov model. Instead of having an underlying Markov chain, hidden Markov random fields have an underlying Markov random field. Suppose that we observe a random variable , where .

  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. Forward–backward algorithm - Wikipedia

    en.wikipedia.org/wiki/Forward–backward_algorithm

    The forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions ::=, …,, i.e. it computes, for all hidden state variables {, …,}, the distribution ( | :).

  9. Category:Hidden Markov models - Wikipedia

    en.wikipedia.org/wiki/Category:Hidden_Markov_models

    Layered hidden Markov model This page was last edited on 30 March 2013, at 04:46 (UTC). Text is available under the Creative Commons Attribution-ShareAlike 4.0 ...