Bayesian Biostatistics - Conclusions

• Likelihood, maximum likelihood, deviance.
• The basic prob. distributions.
• Distinguish the deterministic and stoch. model parts.
• Posterior, prior, likelihood and their connection.
• MCMC
• Credible and prediction intervals.
• Elementary model specification in JAGS.
• GLM, occupancy models, autoregression, random effects.

• ALWAYS start with simple models (and small data).
• Make your models cool and complex only AFTER your simple models run.

Learn your probability distributions. The useful ones are:

• Normal, Poisson, Binomial, Uniform
• Beta, Gamma, Exponential, Negative Binomial, Lognormal
• Categoriacal, Multinomial, Double exponential
• Truncated and censored distributions

• Copy other people's codes and models.
• Copy your own codes and models.

1. Your priors may be too wide.
2. You may need to provide better initial values manually.
3. You have latent variabels - they need good inits.
4. You have mistaken ~ for <-
5. You provide negative \( \lambda \) to Poisson -> log link.
6. Same with Bernoulli -> logit link.
7. Standardize and center your variables, especially for log link.

• If you have a hammer, every problem turns out to be a nail.
• Do not forget the biology for all the stats.