# Tutorial 1: Introduction to Bayesian Statistics

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

- Implement a Gaussian distribution
- Use Bayes' Theorem to find the posterior from a Gaussian-distributed prior and likelihood.
- Change the likelihood mean and variance and observe how posterior changes.
- Advanced (
*optional*): Observe what happens if the prior is a mixture of two gaussians?

- Introduction to Bayesian Statistics
- The Gaussian distribution
- Bayes' Theorem
- Multimodal priors

Experience with Python Programming Language.