omesner / knncmi
This python code estimates conditional mutual information (CMI) and mutual information (MI) for discrete and/or continuous variables using a nearest neighbors approach.
☆35Updated 3 years ago
Alternatives and similar repositories for knncmi:
Users that are interested in knncmi are comparing it to the libraries listed below
- Estimators for the entropy and other information theoretic quantities of continuous distributions☆136Updated 10 months ago
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs☆33Updated 3 years ago
- Python code of Hilbert-Schmidt Independence Criterion☆86Updated 2 years ago
- Classifier based mutual information, conditional mutual information estimation; conditional independence testing☆34Updated 5 years ago
- Granger causality discovery for neural networks.☆210Updated 4 years ago
- Causal discovery for time series☆95Updated 3 years ago
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)☆50Updated 5 years ago
- Differentiable DAG Sampling (ICLR 2022)☆37Updated 2 years ago
- ☆92Updated 2 years ago
- Paper lists for Temporal Point Process☆109Updated 5 months ago
- Code for ICE-BeeM paper - NeurIPS 2020☆86Updated 3 years ago
- (Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information es…☆29Updated 4 years ago
- Statistical Recurrent Unit based time series generative models for detecting nonlinear Granger causality☆33Updated 4 years ago
- A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matr…☆115Updated last year
- Implementation of the MIWAE method for deep generative modelling of incomplete data sets.☆37Updated last year
- Repository for "Differentiable Causal Discovery from Interventional Data"☆72Updated 3 years ago
- Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)☆32Updated 3 years ago
- VAEs and nonlinear ICA: a unifying framework☆34Updated 4 years ago
- TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155☆135Updated 2 years ago
- Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data☆208Updated 3 years ago
- A Pytorch implementation of missing data imputation using optimal transport.☆96Updated 3 years ago
- This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop a…☆59Updated 7 months ago
- Neural Causal Model (NCM) implementation by the authors of The Causal Neural Connection.☆25Updated 2 years ago
- pyCausalFS：A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification☆68Updated 4 years ago
- ☆90Updated 2 years ago
- VAEs and nonlinear ICA: a unifying framework☆47Updated 5 years ago
- An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.☆69Updated last year
- Official code for the ICLR 2021 paper Neural ODE Processes☆72Updated 2 years ago
- Python code of information geometric causal inference☆12Updated 5 years ago
- Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model☆100Updated 4 months ago