Research map / Probabilistic and scientific modeling / Variational and generative sampling
Involutive MCMC: a Unifying Framework
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a persistent directional variable to MALA | MALA · MoG2 | ESS: 0.007±0.002 -> 0.027±0.008 | Helped |
| Add a persistent directional variable to MALA | MALA · Australian | ESS: 0.043±0.001 -> 0.006±0.001 | Hurt |
| Add a persistent directional variable to MALA | MALA · German | ESS: 0.025±0.005 -> 0.004±0.001 | Hurt |
| Add a persistent directional variable to MALA | MALA · Heart | ESS: 0.081±0.012 -> 0.012±0.001 | Hurt |
| Add irreversibility and an auxiliary-variable update to A-NICE-MC | A-NICE-MC · MoG2 | ESS: 0.852±0.239 -> 1.643±0.626 | Helped |
| Add irreversibility and an auxiliary-variable update to A-NICE-MC | A-NICE-MC · Australian | ESS: 0.137±0.026 -> 0.177±0.030 | Helped |
| Add irreversibility and an auxiliary-variable update to A-NICE-MC | A-NICE-MC · German | ESS: 0.032±0.004 -> 0.032±0.004 | No clear effect |
| Add irreversibility and an auxiliary-variable update to A-NICE-MC | A-NICE-MC · Heart | ESS: 0.253±0.033 -> 0.341±0.051 | Helped |
Closest papers by meaning
- AutoStep: Locally adaptive involutive MCMC (ICML 2025 · similarity 0.91)
- Nonparametric Involutive Markov Chain Monte Carlo (ICML 2022 · similarity 0.91)
- Pseudo-Extended Markov chain Monte Carlo (NeurIPS 2019 · similarity 0.90)
- Multi-fidelity Monte Carlo: a pseudo-marginal approach (NeurIPS 2022 · similarity 0.89)
- Gradient-based Adaptive Markov Chain Monte Carlo (NeurIPS 2019 · similarity 0.89)
- Parallelizing MCMC Across the Sequence Length (NeurIPS 2025 · similarity 0.89)
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