Bayesian Biostatistics - Conclusions

Petr Keil
January 2016

You should now be able to use

  • 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.

Some advice

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

Some advice

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

Some advice

  • 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.

And finally

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