AustinRochford

AustinRochford

AustinRochford

AustinRochford

AustinRochford

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AustinRochford

AustinRochford

{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "%matplotlib inline", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"source": "---\ntitle: Maximum Likelihood Estimation of Custom Models in Python with StatsModels\ntags: Statistics, Python\n---", "cell_type": "markdown", "metadata": {}}, {"source": "Maximum likelihood estimation is a common method for fitting statistical models.  In Python, it is quite possible to fit maximum likelihood models using just [`scipy.optimize`](http://docs.scipy.org/doc/scipy/reference/optimize.html). Over time, however, I have come to prefer the convenience provided by [`statsmodels`'](http://statsmodels.sourceforge.net/) [`GenericLikelihoodModel`](http://statsmodels.sourceforge.net/devel/dev/generated/statsmodels.base.model.GenericLikelihoodModel.html).  In this post, I will show how easy it is to subclass `GenericLikelihoodModel` and take advantage of much of `statsmodels`' well-developed machinery for maximum likelihood

AustinRochford

AustinRochford

AustinRochford