Statistics for Brain and Cognitive Science
Course facts
- Payment: Free, no certificate
- Credits: Non Credit
- Learning style: Self-paced, start anytime
- Resources: Lecture Notes; Problem Sets
- Before you start: 9.40 Introduction to Neural Computation and the ability to program in MATLAB ®.
MIT OpenCourseWare details
- As taught in: Fall 2016
- Department: Brain and Cognitive Sciences
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Statistics for brain and cognitive science: probability, estimation, likelihood, Bayesian methods, bootstrap, testing and regression.
Brain and cognitive scientists need statistics to analyze experimental data, read statistical reports in the literature critically and reason under uncertainty. This course gives the basic tools, organized around three bodies of theory: probability, from its axioms through discrete and continuous models to the law of large numbers and the central limit theorem; statistical inference, with estimation, likelihood, Bayesian methods, the bootstrap and Monte Carlo methods, and hypothesis testing; and the linear model, with regression and analysis of variance.
What You Will Achieve
- Work with discrete, continuous and joint probability models
- Estimate parameters by the method of moments, maximum likelihood and Bayesian methods
- Use bootstrap and Monte Carlo methods and test hypotheses
- Fit and interpret simple regression models and analysis of variance
Program Overview
Probability Theory
The probability models behind experimental data.
- Conditional probability, Bayes’ rule and independence
- Discrete and continuous probability models
- Joint and conditional distributions
- Moment generating functions, the law of large numbers and the central limit theorem
Statistical Theory
Estimating quantities from data and judging the evidence.
- Method-of-moments estimation
- Likelihood theory
- Bayesian methods
- Bootstrap, Monte Carlo methods and hypothesis testing
The Linear Model
Relating variables and comparing groups.
- Simple regression
- Analysis of variance
Course Completion
Job Outlook
Statistical analysis is central to research in neuroscience and cognitive science, psychology, clinical research and data science.
Good for: Neuroscience and psychology students who want statistics taught with their own kind of data in mind.
Keep in mind: The materials are lecture notes with supplemental figures and eight homework assignments, so you study from the notes rather than recorded lectures.
Bottom line: A thorough, well-structured statistics course for the brain and cognitive sciences.
Frequently Asked Questions
What are the three parts of the course?
Probability theory, statistical theory, and the linear model, which covers simple regression and analysis of variance.
Does it cover Bayesian statistics?
Yes. Bayesian methods are taught alongside likelihood theory and method-of-moments estimation in the statistical theory part.
What practice is there?
Eight homework assignments that apply the methods to data, plus figures that accompany several lectures.
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