Presentation 15: An Introduction to Parameter Estimation: Technological Companion
Channel: Mathematical Science Research Launchpad
Date: 13/08/2020

This is the technological companion to the video lesson titled: An Introduction to Parameter Estimation (https://youtu.be/qRcgm6gU-4Q).


In this tutorial, apply MATLAB's mle (Maximum Likelihood Estimation) function to the problem of estimating unknown parameters of the binomial, geometric, and Poisson distributions. We estimate the, unknown parameters of the  hypergeometric distributions by implementing our own, method of moments based approach to mark and recapture in MATLAB.  Finally, we also use mle to explore one example of estimating the mean and standard deviation parameters of the normal distribution.

This video lesson supports the Probability and Statistics Core Learning Resource (CLR) (https://mathsciresearchlaunchpad.wordpress.com/probability-and-statistics/) at the Mathematical Science Research Launchpad.

Resources:
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GitHub repository containing Matlab scripts and LiveScripts used in this tutorial: https://github.com/wrightes-msrl/ParameterEstimation