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Navigable Slide Index

1. Introduction
2. Why this course?
3. Course outline
4. Synopsis (part 1)
5. The Bayesian paradigm
6. The Bayesian method (1)
7. The Bayesian method (2)
8. The prior, data and posterior values
9. Example - prior distribution
10. Example - first year's data
11. Example - first year triplot
12. Example - more data
13. Example - new triplot
14. First Bayes software
15. Formulating prior distributions
16. Computing tools
17. Bayesian vs. frequentist
18. Bayesian vs. frequentist: statistical inference
19. Bayesian vs. frequentist: the nature of probability
20. Bayesian vs. frequentist: the nature of parameters
21. The use of prior information in the 2 approaches
22. The nature of inferences
23. Information contained in a posterior distribution
24. Contours of posterior density of two parameters
25. Bayes is better
26. 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

Links

Series:

• Bayesian Methods in Health Economics

Categories:

• Methods

Talk Citation

Publication History

• Published on February 16, 2009

Financial Disclosures

• Prof. Anthony O’Hagan has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.