Markov-switching state space models for uncovering musical interpretation
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Publication:2247457
DOI10.1214/21-AOAS1457zbMath1478.62380arXiv1907.06244OpenAlexW3202007072MaRDI QIDQ2247457
Michael McBride, Daniel J. McDonald, Yupeng Gu, Christopher Raphael
Publication date: 17 November 2021
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1907.06244
Inference from stochastic processes and prediction (62M20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Markov processes: estimation; hidden Markov models (62M05) Applications of statistics (62P99) Markov processes: hypothesis testing (62M02)
Uses Software
Cites Work
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- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Efficient likelihood estimation in state space models
- A sticky HDP-HMM with application to speaker diarization
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Dynamic linear models with Markov-switching
- A hybrid graphical model for rhythmic parsing
- Adaptive piecewise polynomial estimation via trend filtering
- ggplot2
- Was Something Wrong with Beethoven’s Metronome?
- $\ell_1$ Trend Filtering
- Non-Gaussian State-Space Modeling of Nonstationary Time Series
- Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
- Monte Carlo maximum likelihood estimation for non-Gaussian state space models
- Particle Markov Chain Monte Carlo Methods
- On-Line Inference for Hidden Markov Models via Particle Filters
- Approximate Methods for State-Space Models
- Dynamic Nonparametric Bayesian Models for Analysis of Music
- Seamless R and C++ Integration with Rcpp
- An interactive case-based reasoning approach for generating expressive music