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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Proposes adaptive and alternative algorithms to improve the computational efficiency of simulation smoothing for large mixed-frequency VARs in nowcasting applications.
The paper presents performant parallel CPU implementations of tridiagonal factorization for skew-symmetric matrices that exceed prior work via FLAME-derived algorithms and BLIS optimizations.
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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.
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Simulation smoothing for nowcasting with large mixed-frequency VARs
Proposes adaptive and alternative algorithms to improve the computational efficiency of simulation smoothing for large mixed-frequency VARs in nowcasting applications.
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Performant Tridiagonal Factorization of Skew-Symmetric Matrices
The paper presents performant parallel CPU implementations of tridiagonal factorization for skew-symmetric matrices that exceed prior work via FLAME-derived algorithms and BLIS optimizations.