## Questions on Paul Lewis phylogenetics primer part 3a – Introduction
to Bayesian statistics

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.*

- In your own words can you describe each component of Bayes’ rule?
Which parts are difficult to understand?

- Can you describe the difference between discrete and continuous
variables? And between probabilities and probability densities?

- 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.

- What is the aim of MCMC in Bayesian inference? Visit the MCMC robot app
to explore this further.

## Notes

*In this talk, Paul says that most times you don’t need to worry
about the proposal distributions because available software experiments
with the proposals at the beginning of your MCMC run (this process is
called tuning) and tries to optimize the best proposal size for your
model/data. This is true, however, in practice, as data sets become
larger and models become more complex, we sometimes need to pay
attention to these, as convergence can be challenging.*