GitHub - emune-dev/Data-missingness-paper: Code underlying the paper "Missing data in amortized simulation-based neural posterior estimation" (original) (raw)
Navigation Menu
- GitHub Copilot Write better code with AI
- GitHub Models New Manage and compare prompts
- GitHub Advanced Security Find and fix vulnerabilities
- Actions Automate any workflow
- Codespaces Instant dev environments
- Issues Plan and track work
- Code Review Manage code changes
- Discussions Collaborate outside of code
- Code Search Find more, search less
- Explore
- Pricing
Provide feedback
Saved searches
Use saved searches to filter your results more quickly
Appearance settings
Repository files navigation
Data-missingness-paper
This repository contains all code and simulation scripts for the paper "Missing data in amortized simulation-based neural posterior estimation". It is divided into folders dedicated to the conducted numerical experiments.
Regarding the content of these folders:
- The Jupyter notebooks were used to validate/compare the performance of trained missing data approaches and to create illustrative figures for the paper, including convergence plots, posterior plots, error metrics, etc.
- Subfolders with the ending "ckpts" contain the Python script for training a BayesFlow network on a specific forward model, an output file of the running loss as well as the stored networks after the final training epoch.
- Subfolders with the name "bayesflow" contain the implementation of the BayesFlow method (version 0.0.0b1) downloaded from https://github.com/stefanradev93/BayesFlow. In some cases, slight modifications have been made to meet the purpose of our numerical experiment.
Languages
- Jupyter Notebook 99.2%
- Python 0.8%