Hidden Markov models can model complex Markov processes where the states emit the observations according to some probability distribution. One such example of distribution is Gaussian distribution, in such a Hidden Markov Model the states output is represented by a Gaussian distribution.
Moreover it could represent even more complex behavior when the output of the states is represented as mixture of two or more Gaussians, in which case the probability of generating an observation is the product of the probability of first selecting one of the Gaussians and the probability of generating that observation from that Gaussian.
Read more about this topic: Hidden Markov Model
Other articles related to "types, type":
... Claw-types set quickly in most seabeds and although not an articulated design, they have the reputation of not breaking out with tide or wind changes, instead slowly turning in the bottom to align with ... Claw types have difficulty penetrating weedy bottoms and grass ... have to be oversized to compete with other types ...
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... Attempts to introduce types date back to the 1980s, and as of 2008 there are still attempts to extend Prolog with types ... Type information is useful not only for type safety but also for reasoning about Prolog programs ...
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