Research map / Probabilistic and scientific modeling / Variational and generative sampling
Inference Networks for Sequential Monte Carlo in Graphical Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learned neural inference network proposals (inverse factorization, MADE-based) used in place of prior proposals for SMC | Factorial HMM (D=20 devices) · Synthetic factorial HMM, 30 time steps, 100 particles | number of unique surviving ancestries | Helped |
| Learned neural proposals for SMC on divide-and-conquer SMC with neural proposals versus importance sampling from prior and neural proposals | Hierarchical Poisson pump failure model · Power plant pump failure data (George et al., 1993) | convergence of marginal likelihood estimate vs number of particles | Helped |
| Replacing the prior-based proposal with a learned neural proposal in the non-conjugate regression model | Polynomial regression with Student-t likelihood · Synthetic regression data | qualitative match to MCMC posterior | No clear effect |
Closest papers by meaning
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