Bayesian Biostatistics - Conclusions
Petr Keil
Feb 2014
You should now be able to use
- likelihood, maximum likelihood, deviance
- basic prob. distributions
- deterministic vs stoch. model parts
- posterior, prior, likelihood, their connection
- MCMC
- credible and prediction intervals
- model specification in JAGS
- GLM, occupancy models, autoregression, random effects
Advice
- ALWAYS start with simple models.
- Make your models complex only AFTER your simple models run.
Advice
- Copy other people's codes and models.
Advice
- Learn the difference between data and prior.
- Learn to make formal definition of models.
Advice
- Give your data and the code to the readers.
%#&$*! IT STILL DOES NOT RUN
- Your priors are too wide.
- Provide better initial values manually.
- Latent variabels need good inits.
- You have mistaken
~
for <-
- You provide negative \( \lambda \) to Poisson -> log link.
- Same with Bernoulli -> logit link.
- Standardize and center your variables, especially for log link.
And finally:
- If you have a hammer, every problem appears to be a nail.
- Do not forget the biology for all the stats.