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Tutorial 1: Introduction to Bayesian Statistics

Difficulty level

This tutorial provides an introduction to Bayesian statistics and covers developing a Bayesian model for localizing sounds based on audio and visual cues. This model will combine prior information about where sounds generally originate with sensory information about the likelihood that a specific sound came from a particular location. The resulting posterior distribution not only allows us to make optimal decision about the sound's origin, but also lets us quantify how uncertain that decision is. Bayesian techniques are therefore useful normative models: the behavior of human or animal subjects can be compared against these models to determine how efficiently they make use of information.

Overview of this tutorial

  1. Implement a Gaussian distribution
  2. Use Bayes' Theorem to find the posterior from a Gaussian-distributed prior and likelihood.
  3. Change the likelihood mean and variance and observe how posterior changes.
  4. Advanced (optional): Observe what happens if the prior is a mixture of two gaussians?
Topics covered in this lesson
  • Introduction to Bayesian Statistics
  • The Gaussian distribution
  • Bayes' Theorem
  • Multimodal priors

Experience with Python Programming Language.