Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Neural Posterior Estimation and Statistical Model Criticism
Authors: Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian Schmon
NeurIPS 2022 | Venue PDF | 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. |