Presentation 17: Maximum A Posteriori Estimation and Bayesian Learning:  Technological Companion
Channel: Mathematical Science Research Launchpad
Date: 14/08/2020

This is the technological companion to the video lesson titled "Maximum A Posteriori Estimation and Bayesian Learning".


In this tutorial, we use MATLAB to implement two more techniques for parameter estimation: Maximum a Posteriori estiomation (MAP) and Bayesian Learning (BL). Both are applications of Bayes' theorem, and both can be interpreted as novel models for successively refining our belief about the location of the correct value for an unknown parameter as we collect and analyze more and more data that is modeled by a distribution that itself depends upon that parameter. In short, these two techniques echo the process of learning from data. We will compare and contrast the strengths and weaknesses of both techniques as well as illustrate them through examples.

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/MAP-BL