Part 3a – Introduction to Bayesian statistics
Answer the following questions in your own words. Remember - the goal is not to get everything correct. The objective is to develop your understanding of complex models through writing ✍️📈

⚠️ This is probably the most important of the four parts. The most important points to try and grasp from this one are the priors in Bayesian inference and the principles of MCMC ⚠️

Note: if you’re feeling overloaded, you could skip the last 10 minutes of this talk, since you don’t need to worry about Metropolis-coupled MCMC (i.e. heat vs. cold chains) or asymmetric proposals.

Link to the video: Paul Lewis phylogenetics primer part 3a
Link to course website: phylogenetics-fau.netlify.app
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In your own words can you describe each component of Bayes’ rule? Which parts are difficult to understand?
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Can you describe the difference between discrete and continuous variables?
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Can you describe the difference between probabilities and probability densities?
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What is the difference between vague vs. informative priors? Unfortunately Paul’s archery app isn’t available right now, so pay close attention to the demo.
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What is the aim of MCMC in Bayesian inference? Visit the MCMC robot app to explore this further.
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