Research map / Probabilistic and scientific modeling / Molecules and proteins
Inverse Filtering for Hidden Markov Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace MILP-based joint reconstruction with nullspace/linear-algebra solution exploiting HMM filter structure | Inverse filtering for HMMs (Problem 3, unknown sensor and observations) · Noise-free posteriors | computational cost | Helped |
| Cluster nullspace directions (spherical k-means) and rescale via LP instead of exact nullspace solution | Inverse filtering with noise-corrupted posteriors · Sleep segmentation, Gaussian posterior noise | fraction of correctly reconstructed observations | Mixed |
| Increasing noise sigma (decreasing SNR) in posteriors (ablation) | Noisy inverse filtering algorithm · Sleep segmentation, Gaussian posterior noise | error in reconstructed B and fraction of correct observations | Hurt |
| Runtime of proposed algorithm on sleep data | Inverse filtering algorithm · Sleep segmentation (1030 quantized EEG samples) | runtime | Helped |
Closest papers by meaning
- Linear approximations to HMM filtering (NeurIPS 2026 · similarity 0.86)
- Generalised Bayesian Filtering via Sequential Monte Carlo (NeurIPS 2020 · similarity 0.86)
- Learning Hidden Markov Models When the Locations of Missing Observations are Unknown (ICML 2023 · similarity 0.86)
- Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices (NeurIPS 2016 · similarity 0.85)
- Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication (AAAI 2016 · similarity 0.85)
- Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations (ICML 2020 · similarity 0.85)
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