Kiefer-Wolfowitz Nonparametric Empirical Bayes

Compute the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixtures.

In contrast to the previous approaches, the optimization problem is reformulated into a convex problem by Koenker and Mizera (2014)'s method and efficiently solved by interior-point method.

Making Predictions With No Features - A Basic Usage

Given a training set T = {y_i}, the algorithm provides a way to construct a predictor of future y-values such that the sum of squared errors between observations and predictors is minimized.

Getting Started

Prerequisites

You will need:

• python (>= 3.6)
• pip (>= 19.0.3)
• MOSEK (>=8.1.30)

Important about MOSEK:

• MOSEK is a commercial optimization software. Please visit MOSEK for license information.
• PIP:

pip install -f https://download.mosek.com/stable/wheel/index.html Mosek --user

For different ways of installation, please visit their installation page.

• MOSEK needs to be installed in the GLOBAL environment.

Installing

pip install kwnpeb

Examples

• simple - The basic usage
• bayesball - In-season prediction of batting averages with the 2005 Major League baseball

Contributors

• Sile Tao
• Li Zhang
• Guanqi Huang

License

This project is licensed under the MIT License - see the LICENSE.md file for details