# Bayesian statistics

Language English Free Free Beginner

Learn the basics of Bayesian statistics with this free course. Discover how to estimate probabilities, calculate conditional probabilities, and use Bayes’ theorem. Gain a solid understanding of the Bayesian inference process and the role of prior, likelihood, and posterior distributions. Start learning today!

This free course is an introduction to Bayesian statistics. Section 1 discusses several ways of estimating probabilities. Section 2 reviews ideas of conditional probabilities and introduces Bayes’ theorem and its use in updating beliefs about a proposition, when data are observed, or information becomes available. Section 3 introduces the main ideas of the Bayesian inference process. The prior distribution summarises beliefs about the value of a parameter before data are observed. The likelihood function summarises information about a parameter contained in observed data and the posterior distribution represents what is known about a parameter after the data have been observed.

## Course learning outcomes

After studying this course, you should be able to:

Use relative frequencies to estimate probabilities

Calculate conditional probabilities

Calculate posterior probabilities using Bayes’ theorem

Calculate simple likelihood functions

Describe the role of the posterior distribution, the likelihood function and the posterior distribution in Bayesian inference about a parameter Ɵ.

## User Reviews

0.0 out of 5
0
0
0
0
0

There are no reviews yet.

×  