-
hyperdistributions or a
conjugate hyperprior. However, this
rapidly becomes very
abstract and
removed from the
original problem.
Hyperpriors, like
conjugate priors...
-
model is as follows: α ∼ A
Dirichlet hyperprior,
either a
constant or a
random variable β ∼ A
Dirichlet hyperprior,
either a
constant or a
random variable...
- distribution, namely: Hyperparameters:
parameters of the
prior distribution Hyperpriors:
distributions of
Hyperparameters Suppose a
random variable Y follows...
- take a
probability distribution on the
hyperparameter itself,
called a
hyperprior. One
often uses a
prior which comes from a
parametric family of probability...
-
Uncertainty about these hyperparameters can, in turn, be
expressed as
hyperprior probability distributions. For example, if one uses a beta distribution...
- example, when
there are
multiple Dirichlet priors related by the same
hyperprior. Each
Dirichlet prior can be
independently collapsed and
affects only...
-
estimates of the variance).
Bayes estimator Bayesian network Hyperparameter Hyperprior Best
linear unbiased prediction Robbins lemma Spike-and-slab variable...
-
distributions whose parameters are
drawn from a higher-level
distribution (
hyperpriors).
Starting with
studies such as
Lichtenstein &
Slovic (1971), it was...
- prior, here a
combination of two beta distributions; this is a form of
hyperprior. An
arbitrary likelihood will not
belong to an
exponential family, and...
-
distribution given a
collection of N samples. Intuitively, we can view the
hyperprior vector α as pseudocounts, i.e. as
representing the
number of observations...
