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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. 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.

  4. Map matching - Wikipedia

    en.wikipedia.org/wiki/Map_matching

    Map matching is described as a hidden Markov model where emission probability is a confidence of a point to belong a single segment, and the transition probability is presented as possibility of a point to move from one segment to another within a given time. [10] [11]

  5. 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]

  6. 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.

  7. 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 .

  8. Hankel matrix - Wikipedia

    en.wikipedia.org/wiki/Hankel_matrix

    Hankel matrices are formed when, given a sequence of output data, a realization of an underlying state-space or hidden Markov model is desired. [3] The singular value decomposition of the Hankel matrix provides a means of computing the A, B, and C matrices which define the state-space realization. [4]

  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 ...