A Markov embedding of ranked unlabelled trees reduces state space, enabling efficient Fréchet means, arbitrary-order F-matrix moments via phase-type theory, and improved neutrality tests under coalescent models.
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QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
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Markov embedding of ranked unlabelled evolutionary trees and its applications
A Markov embedding of ranked unlabelled trees reduces state space, enabling efficient Fréchet means, arbitrary-order F-matrix moments via phase-type theory, and improved neutrality tests under coalescent models.
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Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.