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{{Short description|Multilevel, non-directly observable 'probability engine'}}
A layered hidden Markov model consists of ''N'' levels of HMMs, where the HMMs on level ''i'' + 1 correspond to observation symbols or probability generators at level ''i''. Every level ''i'' of the LHMM consists of ''K''<sub>''i''</sub> HMMs running in parallel.<ref>N. Oliver, A. Garg and E. Horvitz, "Layered Representations for Learning and Inferring Office Activity from Multiple Sensory Channels", Computer Vision and Image Understanding, vol. 96, p. 163–180, 2004.
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== Background ==
LHMMs are sometimes useful in specific structures because they can facilitate learning and generalization. For example, even though a fully connected HMM could always be used if enough [[Training, validation, and test data sets|training data]] were available, it is often useful to constrain the model by not allowing arbitrary state transitions. In the same way it can be beneficial to embed the HMM in a layered structure which, theoretically, may not be able to solve any problems the basic HMM cannot, but can solve some problems more efficiently because less training data is needed.
== The layered hidden Markov model ==
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