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Measuring the Influence of Observations in HMMs through the Kullback-Leibler Distance

We measure the influence of individual observations on the sequence of the hidden states of the Hidden Markov Model (HMM) by means of the Kullback-Leibler distance (KLD). Namely, we consider the KLD between the conditional distribution of the hidden states' chain given the complete sequence of observations and the conditional distribution of the hidden chain given all the observations but the one under consideration. We introduce a linear complexity algorithm for computing the influence of all the observations. As an illustration, we investigate the application of our algorithm to the problem of detecting outliers in HMM data series.

preprint2012arXivOpen access
Vittorio PerducaGregory Nuel
math.ITInformation TheoryMachine Learningmath.PR
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Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Vittorio PerducaGregory Nuel

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