Dynamic Bayesian Networks Representation, Inference And by Kevin Patrick Murphy

By Kevin Patrick Murphy

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It is straightforward to modify the CPD definitions to cause this behavior. The above solution is somewhat inefficient since it introduces an extra dummy value, thus increasing the size of the state space of each level by one. 5). , parametrically) redundant. It is straightforward to modify the CPD definitions to cause this behavior. Advantages of representing the HHMM as a DBN There are several advantages of representing the HHMM as a DBN rather than using the original formulation [FST98] in terms of nested state-transition diagrams.

One concern is that this might make inference intractable. 4) into one “mega” variable, and then use a (modified) forwards-backwards procedure, which has complexity O(Tout N 2 ), where N is the number of states of the mega variable, and Tout is the length (number of frames) of the output (observed) sequence. 21 are as follows: W th ∈ {1, . . , Tin }, Qht ∈ {1, . . , P }, St ∈ {1, . . , K}, FtW ∈ {0, 1}, FtS ∈ {0, 1}, where P is the max number of states in any word model, and K is the maximum number of states in each phone HMM (typically 3).

The state in the first slice of a new segment is independent of the state in the last slice of the previous segment. ) Hence we can reason about segment independently (conditioned on knowing the boundaries), by running K Kalman filters per segment; we use the forwards-backwards algorithm to figure out boundary locations. For details, see [DRO93]. 33 modelled as a DBN. Discrete nodes are squares, continuous nodes are circles. Abbreviations: R = resistance, P = pressure, F = flow, M = measurement, RF = resistance failure, MF = measurement failure.

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