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).

## Introductory material

Submarine Activity: click here

The likelihood function

- Maximum likelihood estimation (MLE)

Biomass recovery activity: click here

## Basics of Bayes

- Conjugate likelihood-prior pairs:

- Basketball example I: Binomial-beta pair
- Basketball example II: Informative priors
- Basketball example III: Final remarks

Cancer risk activity: click here

Monte Carlo integration

- Full conditional distributions
- Customized Gibbs sampler in R
- Tips on troubleshooting your customized Gibbs sampler

Climate change activity: click here

- Convergence

Population size activity: description and activity

Educational data activity: click here

- Metropolis-Hastings algorithm

Poisson regression activity: click here

- Mixed models

Radon activity: click here

- Sources of uncertainty

- Example of river pollutant
- Example of river pollutant across a range of covariate values
- Model fit and predictive distribution

River pollutant activity: click here

## Less common but interesting models

- Robust regression

Robust regression activity: click here

- Models with latent continuous responses

Censored data activity: click here

## Troubleshooting algorithms and models

- Improving MCMC algorithms by centering covariates

Sensitive question activity: click here

- Common problems in JAGS (and other MCMC algorithms)