Next topic

FEM for discrete data

Free Energy Minimization

Quick Start

Installation

  • To install fem:

    pip install fem
    

    Note

    This package requires a fortran compiler to compile an external module; a popular, free choice is gfortran. This package also depends on the the lapack development files.

  • Load fem in your Python script:

    import fem
    

Interactive notebook

Launch an interactive Jupyter notebook using Binder to run and edit the examples in the documentation:

https://mybinder.org/badge.svg

Introduction

Free energy minimization (FEM) is a method for learning from data the probability distribution \(p\), with a form inspired by statistical physics, of an output variable \(y\) given input variables \(x_i\). We use \(p\) to both 1) understand the relations among the data variables \(x_i,y\), for example, to identify pairs or groups of variables that vary together and 2) to predict the output given new inputs. We are actively developing variations of the method that are conducive to modeling different types of data.