Research map / Learning theory and algorithms / Clustering
Bayesian Model Selection for Change Point Detection and Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the two-pass dynamic programming algorithm for joint change-point detection and clustering | piecewise-constant simulated signal, N=2000 and S/N = 1 · Simulated signal (300 simulations) | change points correctly detected / change points detected: 0.8528 | Helped |
| Apply the second dynamic programming pass to cluster segments found in the first pass | Two-Pass Dynamic Programming Algorithm · Difficult simulated signal (N=500, d''_{m^*}= 13) | false change points retained | Helped |
| Use the proposed algorithm at low signal-to-noise ratios | piecewise-constant simulated signal · Simulated signal with S/N=0.5 | signal reconstruction: S/N=0.5 | Helped |
| Reduce observations per segment by scaling the signal to N=500 | Two-Pass Dynamic Programming Algorithm · Difficult simulated signal (N=500, d''_{m^*}= 13) | change-point location accuracy: N=500 and d''_{m^*}= 13 | Hurt |
Closest papers by meaning
- Conjugate Bayesian Two-step Change Point Detection for Hawkes Process (NeurIPS 2024 · similarity 0.86)
- Optimal Online Change Detection via Random Fourier Features (NeurIPS 2025 · similarity 0.86)
- Reducing sequential change detection to sequential estimation (ICML 2024 · similarity 0.86)
- Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation (AAAI 2024 · similarity 0.85)
- The Sample Complexity of Multiple Change Point Identification under Bandit Feedback (NeurIPS 2026 · similarity 0.85)
- Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing (ICML 2024 · similarity 0.85)
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