Robust Neural Posterior Estimation and Statistical Model Criticism
Authors: Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian Schmon
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess the approach on a range of artificially misspecified examples, and find RNPE performs well across the tasks, whereas naïvely using NPE leads to misleading and erratic posteriors. |
| Researcher Affiliation | Collaboration | 1School of Mathematics, Bristol University, UK 2Improbable, UK 3School of Biological Sciences, Bristol University, UK 4Department of Mathematical Sciences, Durham University, UK |
| Pseudocode | Yes | Pseudo-code for the overall approach is given in Algorithm 1. |
| Open Source Code | Yes | The code required to reproduce all the results from this manuscript is available at https://github.com/danielward27/rnpe. |
| Open Datasets | No | The paper describes generating 'N = 50,000 simulations' and '1000 different observations and ground truth parameter pairs' for its tasks, but does not refer to or provide access information for any pre-existing, publicly available datasets. |
| Dataset Splits | No | The paper mentions training on 'N = 50,000 simulations' and evaluating on '1000 different observations', but does not specify explicit train/validation/test splits for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments in the provided text. |
| Software Dependencies | No | The paper mentions software like 'Num Pyro python package', 'JAX', 'Equinox', and 'Numba', but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | For all experiments, we used N = 50,000 simulations, with M = 100,000 MCMC samples following 20,000 warm up steps. The MCMC chains were initialised using a random simulation, and zj = 1 for j = 1, . . . , D. To build the approximation q(x), we used block neural autoregressive flows (De Cao et al., 2020). For the approximation of q(θ | x), we used neural spline flows (Durkan et al., 2019). For all tasks the hyperparameters were kept consistent; information on hyperparameter choices can be found in Appendix C. |