atlas

Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes

Robust Neural Posterior Estimation and Statistical Model Criticism

NeurIPS 2022 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Simulation-based inference · 9 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace NPE with RNPE (spike-and-slab error model, denoising)NPE · SIR betaMSE: 1.10 -> 0.13Helped
replace NPE with RNPENPE · Gaussian muMSE: 0.17 -> 0.02Helped
replace NPE with RNPENPE · CS lambda_dMSE: 1.92 -> 1.21Helped
replace NPE with RNPENPE · Gaussian LinearMSE: 0.75 -> 0.70No clear effect
replace NPE with RNPENPE · CS lambda_cMSE: 0.12 -> 0.13No clear effect
explicitly denoise with learned q(x) (RNPE) instead of adding the error model inside the simulator (NNPE)NNPE · misspecified tasks, log prob of true parameterslog probability of true parametersHelped
replace NPE with RNPENPE · misspecified tasks, coverageposterior coverageHelped
use RNPE on a well-specified simulatorNPE · well-specified taskslog probability of true parametersHurt

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy