Exam Notes Flashcards
(71 cards)
Exchangeability
Prior predictive density
Posterior predictive density
De finetti rep theorem
reg expo family
Conjugate prior for regular expo family
Frequentist inference models (distributions)
Bayesian inference for normal models with know var
Bayesian inference for normal with both shape and scale unknown
Likelihood principle
Define uninformative priors
Improper prior
Uninformative prior are not
Invariant under reparameterisation
Jeffrey’s prior is ___ and is not ___
Is invariant under reparam
Not upholding likelihood principle
Define Bayesian point estimator
Show that quadratic loss gives mean
Show that solute loss gives median
Show that 0-1 loss gives mode and when do we use?
Use for discrete, mode is maximum a posterior
Define frequentis CI
Define Bayesian CI
Define quantile intervals
Define HPD
Highest posterior density
MC integration for posterior mean, expectation of function of params and for posterior prob
Define posterior consistency
