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  2. Hidden Markov model - Wikipedia

    en.wikipedia.org/wiki/Hidden_Markov_model

    A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as ). An HMM requires that there be an observable process Y {\displaystyle Y} whose outcomes depend on the outcomes of X {\displaystyle X} in a known way.

  3. Multiple sequence alignment - Wikipedia

    en.wikipedia.org/wiki/Multiple_sequence_alignment

    A profile hidden Markov model (HMM) modelling a multiple sequence alignment. A hidden Markov model (HMM) is a probabilistic model that can assign likelihoods to all possible combinations of gaps, matches, and mismatches, to determine the most likely MSA or set of possible MSAs. HMMs can produce a single highest-scoring output but can also ...

  4. 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.

  5. Hierarchical hidden Markov model - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_hidden_Markov...

    The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM). In an HHMM, each state is considered to be a self-contained probabilistic model. More precisely, each state of the HHMM is itself an HHMM. HHMMs and HMMs are useful in many fields, including pattern recognition. [1] [2]

  6. 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 ( | :).

  7. Sequence labeling - Wikipedia

    en.wikipedia.org/wiki/Sequence_labeling

    This leads naturally to the hidden Markov model (HMM), one of the most common statistical models used for sequence labeling. Other common models in use are the maximum entropy Markov model and conditional random field.

  8. Dynamic time warping - Wikipedia

    en.wikipedia.org/wiki/Dynamic_time_warping

    Another related approach are hidden Markov models (HMM) and it has been shown that the Viterbi algorithm used to search for the most likely path through the HMM is equivalent to stochastic DTW. [24] [25] [26] DTW and related warping methods are typically used as pre- or post-processing steps in data analyses.

  9. 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.