Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Abstract: Existing algorithms for estimating the model parameters of an explicit-duration hidden Markov model (HMM) usually require computations as large as O((MD/sup 2/ + M/sup 2/)T) or O(M/sup 2/ DT ...

Abstract: This paper presents a novel star pattern recognition algorithm based on a discrete hidden Markov model (HMM) for autonomous spacecraft attitude determination in the lost-in-space mode. A two ...

Many of the insights hitting soccer pitches today trace back to Jesse Davis and a team of computer scientists open-sourcing tools for some of the sport’s trickiest problems.

Expression-based systems that use only native alignments tend to produce exon-intron structures that are quite accurate. Their primary limitation is that there are many genomes for which little or no ...

After several examples of undisclosed alterations in reagent suppliers’ antibody catalogues surfaced, researchers call for transparency to rebuild trust.

An additional section surveys related model classes -- hidden Markov models, Markov games, constrained MDPs, semi-Markov decision processes, and model-free reinforcement learning -- that lie beyond ...

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