General Information

This repostiory contains the legacy code for the paper:

"BayesFlow: Learning complex stochastic models with invertible neural networks"

All examples and figures from the paper can be explored and recreated by running the corresponding notebooks.

Data for the seqRNA example is available on demand.

New examples and applications can apply the models by simply importing the ready-made models for and functions for online or offline learning.

NOTE: A general and more modular version of the library has moved to https://github.com/stefanradev93/BayesFlow

Requirements

We recommend donwloading the Anaconda distribution of Python and then installing TensorFlow:

$ pip install tensorflow==1.13.1

Name		Name	Last commit message	Last commit date
Latest commit History 102 Commits
Neural_Density		Neural_Density
checkpoints		checkpoints
metrics		metrics
results		results
.gitignore		.gitignore
BayesianRegression_cINN_Invariant_Example.ipynb		BayesianRegression_cINN_Invariant_Example.ipynb
GMM_cINN_Example.ipynb		GMM_cINN_Example.ipynb
Levy_cINN_Invariant_Example.ipynb		Levy_cINN_Invariant_Example.ipynb
Lotka-Volterra_cINN.ipynb		Lotka-Volterra_cINN.ipynb
MVN_Gaussian_cINN_Example.ipynb		MVN_Gaussian_cINN_Example.ipynb
README.md		README.md
Ricker_Comparison.ipynb		Ricker_Comparison.ipynb
Ricker_cINN_LSTM_Example.ipynb		Ricker_cINN_LSTM_Example.ipynb
SIR_cINN_Conv1D_Example.ipynb		SIR_cINN_Conv1D_Example.ipynb
abc_random_forest_ricker.R		abc_random_forest_ricker.R
diagnostics.py		diagnostics.py
diffusion.pyx		diffusion.pyx
inn_tests.py		inn_tests.py
inn_utils.py		inn_utils.py
losses.py		losses.py
models.py		models.py
multiscale_agent_based.ipynb		multiscale_agent_based.ipynb
process_models.py		process_models.py
seqRNA_cINN_Attention_Example.ipynb		seqRNA_cINN_Attention_Example.ipynb
setup.py		setup.py
simulate_diffusion.py		simulate_diffusion.py
simulate_splat.R		simulate_splat.R
viz.py		viz.py