Research map / Probabilistic and scientific modeling / Variational and generative sampling
Moment-Based Variational Inference for Markov Jump Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use one time-dependent intensity scaling factor per reaction channel | Moment-based variational inference · Gene expression model | gene activation detection true positive fraction: 0.94 | Helped |
| Use one time-dependent intensity scaling factor per reaction channel | Moment-based variational inference · Gene expression model | gene activation detection false positive fraction: 0.15 | Mixed |
| Use natural-gradient updates instead of raw gradient updates | Moment-based variational inference · Examples considered in the paper | optimization speed and reliability | Helped |
| Use one time-dependent factor per reaction channel | Moment-based variational inference · Gene expression model | posterior variance calibration at observation times | Hurt |
| Apply moment closure to the non-closed predator-prey moment equations | Moment-based variational inference · Predator-prey model | posterior variance relative to exact smoothing | Hurt |
Closest papers by meaning
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- Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models (NeurIPS 2026 · similarity 0.87)
- Variational Inference for SDEs Driven by Fractional Noise (ICLR 2024 · similarity 0.87)
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