Information
Free
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
Want to find out more about this course? Join our Conservation Careers Academy to view the full details of this course, along with over 12,000 conservation jobs, courses, internships and volunteer placements each year globally, along with many other career-boosting benefits