Bayesian Biostatistics - Conclusions
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.
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.
%#&$*! IT STILL DOES NOT RUN
Your priors may be too wide.
You may need to provide better initial values manually.
You have latent variabels - they need good inits.
You have mistaken
You provide negative \( \lambda \) to Poisson -> log link.
Same with Bernoulli -> logit link.
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.