Statistical Inference: Bayesian Approach, Part 2 of 2
To Know
About this Class
In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering this two-part online training for non-statisticians interested in understanding the basic, intuitive thinking behind the two schools of statistical inference: frequentist (known as classical) and Bayesian.
This one-and-a-half-hour online training will address the Bayesian approach and will cover the concepts of Bayes’ Theorem, prior and posterior distributions, and Bayes factor. Technical details will be kept to an absolute minimum.
By the end of this training, attendees will be able to:
- Explain the fundamental concepts of Bayesian inference, including Bayes’ Theorem and its applications.
- Describe the roles of prior and posterior distributions in Bayesian analysis.
- Interpret the Bayes factor and its use in comparing statistical models.
Attendees are not expected to have any prior knowledge to be successful in this training. Although you may attend only one part of this series, attending both parts will give you a better sense of the contrast between these two statistical approaches.
Part 1 is a pre-requisite for this class. You must register separately for Part 1 of this class series.