This website contains part of the material I use for the semester-long course “Introduction to Bayesian statistics applied to life sciences” that I teach at Univ. of Florida. This course is geared towards students that have not been formally trained as statisticians and therefore it does not rely on linear algebra or advanced calculus. However, this material will require some understanding of basic calculus concepts and distribution theory as well as good grasp of programming.

Observation: I am still putting this website together so some of the material is not posted yet and there might be some rough edges. If you have any suggestions or comments, feel free to contact me (drvalle at ufl dot edu).

- The likelihood function

- Maximum likelihood estimation (MLE)

- Conjugate likelihood-prior pairs: a basketball example

- Monte Carlo integration

- MCMC convergence
- Customized Gibbs sampler in R
- Tips on troubleshooting your customized Gibbs sampler
- JAGS
- Interesting JAGS example

- Metropolis-Hastings algorithm

- Different sources of uncertainty and the predictive distribution

FCDs for regression parameters

Model for left censored data

Probit regression model