Sequential monte carlo samplers
About
This paper shows how one can use Sequential Monte Carlo methods to perform what is typically done using Markov chain Monte Carlo methods. This leads to a general class of principled integration and genetic type optimization methods based on interacting particle systems.
Pierre Del Moral, Arnaud Doucet• 2002
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Unconditional modeling | Funnel d = 10 | Delta log Z0.561 | 30 | |
| Unconditional modeling | 25GMM d = 2 | Delta Log Z0.569 | 30 | |
| Unconditional modeling | Manywell d = 32 | Δ log Z14.99 | 29 | |
| Sampling from synthetic distributions | 25GMM d = 2 | Delta Log Partition Function Error (Zr)0.345 | 13 | |
| Sampling from synthetic distributions | Manywell d = 32 | Partition Function Error (Zr)30.17 | 13 | |
| Toy target distribution sampling | Rings d = 2 | Entropy-Reg W2 (eps=0.05)0.18 | 7 | |
| Toy target distribution sampling | Funnel d = 10 | KS Distance0.035 | 7 | |
| Bayesian Logistic Regression | Sonar d=34 | Avg. Posterior Log-Likelihood-111 | 7 | |
| Bayesian Logistic Regression | Ionosphere (d=61) | Avg Posterior Log-Likelihood-87.82 | 7 | |
| Bayesian Logistic Regression | Ionosphere d = 35 (test) | Predictive Likelihood-87.79 | 7 |
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