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Printable Handouts
Navigable Slide Index
- Introduction
- Why this course?
- Course outline
- Synopsis (part 1)
- The Bayesian paradigm
- The Bayesian method (1)
- The Bayesian method (2)
- The prior, data and posterior values
- Example - prior distribution
- Example - first year's data
- Example - first year triplot
- Example - more data
- Example - new triplot
- First Bayes software
- Formulating prior distributions
- Computing tools
- Bayesian vs. frequentist
- Bayesian vs. frequentist: statistical inference
- Bayesian vs. frequentist: the nature of probability
- Bayesian vs. frequentist: the nature of parameters
- The use of prior information in the 2 approaches
- The nature of inferences
- Information contained in a posterior distribution
- Contours of posterior density of two parameters
- Bayes is better
- Concluding remarks
Topics Covered
- Introduction to the Bayesian approach to statistics
- The Bayesian method is presented and illustrated with an example in health
- Bayesian approach is contrasted to the more familiar frequentist approach
- Fundamental differences are explored in terms of different interpretations of probability
- Different understanding of parameters
- The use of prior information and different interpretations of inferences
- It is argued that the Bayesian approach makes better use of information and provides more natural and more meaningful inferences
Talk Citation
O’Hagan, A. (2009, February 16). Bayesian principles [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved January 27, 2021, from https://hstalks.com/bs/1144/.Publication History
Financial Disclosures
- Prof. Anthony O’Hagan has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Bayesian principles
Published on February 16, 2009
43 min