Is that really the pattern we're looking for? Bridging the gap between statistical uncertainty and dynamic programming algorithms
Seminar Room 1, Newton Institute
Two approaches to statistical pattern detection, when using hidden or latent variable models, are to use either dynamic programming algorithms or Monte Carlo simulations. The first produces the most likely underlying sequence from which patterns can be detected but gives no quantification of the error, while the second allows quantification of the error but is only approximate due to sampling error. This paper describes a method to determine the statistical distributions of patterns in the underlying sequence without sampling error in an efficient manner. This approach allows the incorporation of restrictions about the kinds of patterns that are of interest directly into the inference framework, and thus facilitates a true consideration of the uncertainty in pattern detection.
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