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Compute the Kiefer-Wolfowitz nonparametric maximum likelihood estimator

Project description

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

License

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

Project details


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