Statlect is a collection of lectures on probability theory, mathematical statistics, matrix algebra and machine learning. It is offered as a free service to the mathematical community.

Statlect is written by Marco Taboga, an expert in mathematical and statistical methods applied to finance. Marco is a Director in the Economics and Statistics Department of the Bank of Italy, where he has led teams of PhDs in mathematics and economics. Marco holds a PhD in applied mathematics and a Master in finance from the London School of Economics and Political Science. He has taught probability, statistics and machine learning in university and executive courses. He has published several articles on quantitative finance and financial econometrics in top scholarly journals, including Mathematical Finance, Journal of Financial Econometrics, The Journal of Money Credit and Banking and International Finance. Marco's latest research on deep learning for bond pricing is going to be published in the Journal of Financial Econometrics. His recent work on extreme gradient boosting and stacked generalization for dimensionality reduction in linear regression models has been published in Empirical Economics. You can read more about Marco on his LinkedIn page and have a look at his scientific articles on his Google Scholar page.

Statlect lectures have been in the recommended reading lists of statistics classes in several universities, including Dartmouth College, Michigan State University, University of North Carolina - Chapel Hill, Stanford University, University of Texas - Austin, Yale University, Washington University, University of Wisconsin, as well as in many other universities both in the US and in the rest of the world.

The probability and statistics book derived from Statlect lectures is often cited in scientific articles published in engineering journals. For more information, see the entry for the book "Lectures on probability theory and mathematical statistics" in its author's Google Scholar page.

Despite being freely accessible, Statlect is copyrighted. This means that users of this site can read it for free, but they cannot reproduce it without authorization. This is because the creator of Statlect likes to maintain full ownership of its creature.

Statlect is actively maintained and developed by its author. New lectures are continuously added to the website and existing lectures are revised, in order to make them clearer and more complete and to eliminate (inevitable) mistakes.

Statlect visitors often send comments and suggestions that help to improve the lectures. So, the more visitors there are, the better the lectures become. To help Statlect grow, please post a link to Statlect on your blog or personal web page.

Each article on Statlect has a final section that shows how to cite the article itself.

The books

Most of the learning materials found on this website are now available in a traditional textbook format.

Featured pages

- Hypothesis testing
- Gamma distribution
- Wishart distribution
- Convergence in distribution
- Beta function
- Exponential distribution

Explore

Main sections

- Mathematical tools
- Fundamentals of probability
- Probability distributions
- Asymptotic theory
- Fundamentals of statistics
- Glossary

About

Glossary entries

- Discrete random variable
- Almost sure
- Type II error
- Probability space
- Probability mass function
- Mean squared error

Share

- To enhance your privacy,
- we removed the social buttons,
- but
**don't forget to share**.