# Bayesian Statistics

Category
Level
Beginner

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models.

This course introduces the concept of Bayesian statistics and explains why Bayesian statistics are relevant to studying the brain.

Course Features
Lectures
Tutorials
Recordings of question and answer sessions
Lessons of this Course
1
1
Duration:
31:38

This lecture introduces the concept of Bayesian statistics and explains why Bayesian statistics are relevant to studying the brain.

2
2
Duration:
5:13

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.

3
3
Duration:
4:22

In this tutorial, we will use the concepts introduced in Tutorial 1 as building blocks to explore more complicated sensory integration and ventriloquism!

4
4
Duration:
2:40

This tutorial covers computing all the necessary steps to perform model inversion (estimate the model parameters such as 𝑝𝑐𝑜𝑚𝑚𝑜𝑛 that generated data similar to that of a participant). We will describe all the steps of the generative model first, and in the last exercise we will use all these steps to estimate the parameter 𝑝𝑐𝑜𝑚𝑚𝑜𝑛 of a single participant using simulated data. The generative model will be the same Bayesian model we have been using throughout tutorial 2: a mixture of Gaussian prior (common + independent priors) and a Gaussian likelihood.

5
5
Duration:
5:10

This tutorial focuses on Bayesian Decision Theory, which combines the posterior with cost functions that allow us to quantify the potential impact of making a decision or choosing an action based on that posterior. Cost functions are therefore critical for turning probabilities into actions!

6
6
Duration:
26:01
Speaker:

This lecture focuses on advanced uses of Bayesian statistics for understanding the brain.