Multi-fidelity Monte Carlo: a pseudo-marginal approach
Authors: Diana Cai, Ryan P. Adams
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the pseudo-marginal multi-fidelity MCMC approach to several problems, including log-Gaussian Cox process modeling, Bayesian ODE system identification, PDE-constrained optimization, and Gaussian process parameter inference. |
| Researcher Affiliation | Academia | Diana Cai Department of Computer Science Princeton University dcai@cs.princeton.edu Ryan P. Adams Department of Computer Science Princeton University rpa@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Multi-fidelity Monte Carlo with sign-correction |
| Open Source Code | Yes | 3. If you ran experiments...(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix F. |
| Open Datasets | Yes | We apply multi-fidelity and single-fidelity ESS algorithms to a coal mining disasters data set (Carlin et al. [9]). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we use a random-walk M-H update to sample from the conditional K|θ, and truncation distribution µ(K) = geometric(K; γ0). |