13.5 Maximum likelihood and Bayesian inference
- Likelihood:
p(y∣β,X)=n∏i=1(logit−1(Xiβ))yi(1−logit−1(Xiβ))1−yi
* The β that maximizes this is the can be found by iterative techniques (implemented by glm
for example)
Bayesian inference with uniform prior
with
prior=NULL, prior_intercept=NULL
this is same as maximum likelihoodBenefit is you get simulations of full posterior (not just maximum)!
But don’t do this, use priors! (At minimum provides some regularization)
stan_glm
by default uses weakly informative priorsIf prior information is available, use it!