hmm is a Python module designed to work with Hidden Markov Models. The usual algorithms are implemented using Numeric Python.
Hidden Markov Model. A Hidden Markov Model is a statistical model of the distribution of "hidden" features, such as phonemes or part-of-speech tags, based on observable features, such as acoustic segments, or words. The computational models can be automatically trained from data samples, and then used to recognize the "hidden" layer, based on the statistical model derived from the training data.
Hidden Markov Model. A probabilistic model consisting of a number of interconnecting states. Like profiles, HMMs encode full domain alignments. They are essentially linear chains of match, delete or insert states: a match state denotes a conserved column in an alignment; an insert state allows insertions relative to match states; and delete states allow match positions to be skipped.
Hidden Markov model HMMs are statistical models of the sequence consensus of an homologous family (see the docu). A particular class of HMMs has been shown to be equivalent to generalised profiles (8867839). Applications of HMMs to sequence analysis are nicely provided by HMMer and SAM.
HMM Hidden Markov Modelling, a technique widely used in speech recognition systems.
Hidden Markov model. A joint statistical model for an ordered sequence of variables. The result of stochastically perturbing the variables in a Markov chain (the original variables are thus "hidden"), where the Markov chain has discrete variables which select the "state" of the HMM at each step. The perturbed values can be continuous and are the "outputs" of the HMM. A Hidden Markov Model is equivalently a coupled mixture model where the joint distribution over states is a Markov chain. Hidden Markov models are valuable in bioinformatics because they allow a search or alignment algorithm to be trained using unaligned or unweighted input sequences; and because they allow position-dependent scoring parameters such as gap penalties, thus more accurately modeling the consequences of evolutionary events on sequence families.
Hidden Markov Modeling. A numeric analysis which determines the probability of the next item in a string of items. Used frequently in continuous-speech recognition systems.
Hidden Markov models. A computer algorithm which locates the essential, unique features which can distinguish a protein or gene family by analyzing a range of known sequences from the family. These features then are used to locate similar characteristics in unknown sequences.