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Model Inference with Hidden Variables

Introduction

This work was motivated by the fact that real-world data often contains only subsets of variables. Hidden variables can influence the observed ones. Ignoring hidden variables in network inference may lead to poor prediction results. Our ultimate goal was to develop a data-driven method that can use the configurations of observed variables to infer the interactions (observed-to-observed, hidden-to-observed, observed-to-hidden, and hidden-to-hidden), the configuration of hidden variables, and the number of hidden variables.

The Jupyter notebook presents the core idea of our method and an example with simulated data. The application to analyzing experimental data such as recordings of neuronal activity and the stock market are shown in our paper.

Interactive notebook

Use Binder to run our code online. You are welcome to change the parameters and edit the jupyter notebooks as you want.

image

Code Documentation

https://nihcompmed.github.io/hidden-variable

Code Source

https://github.com/nihcompmed/hidden-variable

Reference

Danh-Tai Hoang, Junghyo Jo and Vipul Periwal, "Data-driven inference of hidden nodes in networks", arXiv:1901.04122.