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• W2128889245 abstract "Motivated by the unceasing interest in hidden Markov models (HMMs), this paper reexamines hidden path inference in these models, using primarily a risk-based framework. While the most common maximum a posteriori (MAP), or Viterbi, path estimator and the minimum error, or Posterior Decoder (PD) have long been around, other path estimators, or decoders, have been either only hinted at or applied more recently and in dedicated applications generally unfamiliar to the statistical learning community. Over a decade ago, however, a family of algorithmically defined decoders aiming to hybridize the two standard ones was proposed elsewhere. The present paper gives a careful analysis of this hybridization approach, identifies several problems and issues with it and other previously proposed approaches, and proposes practical resolutions of those. Furthermore, simple modifications of the classical criteria for hidden path recognition are shown to lead to a new class of decoders. Dynamic programming algorithms to compute these decoders in the usual forward-backward manner are presented. A particularly interesting subclass of such estimators can be also viewed as hybrids of the MAP and PD estimators. Similar to previously proposed MAP-PD hybrids, the new class is parameterized by a small number of tunable parameters. Unlike their algorithmic predecessors, the new risk-based decoders are more clearly interpretable, and, most importantly, work out-of-the box in practice, which is demonstrated on some real bioinformatics tasks and data. Some further generalizations and applications are discussed in the conclusion." @default.
• W2128889245 created "2016-06-24" @default.
• W2128889245 creator A5060111294 @default.
• W2128889245 creator A5079016144 @default.
• W2128889245 date "2014-01-01" @default.
• W2128889245 modified "2023-09-23" @default.
• W2128889245 title "Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models" @default.
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