- In
Bayesian statistics, a
hyperprior is a
prior distribution on a hyperparameter, that is, on a
parameter of a
prior distribution. As with the term hyperparameter...
-
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...
- take a
probability distribution on the
hyperparameter itself,
called a
hyperprior. One
often uses a
prior which comes from a
parametric family of probability...
- distribution, namely: Hyperparameters:
parameters of the
prior distribution Hyperpriors:
distributions of
Hyperparameters Suppose a
random variable Y follows...
-
estimates of the variance).
Bayes estimator Bayesian network Hyperparameter Hyperprior Best
linear unbiased prediction Robbins lemma Spike-and-slab variable...
- example, when
there are
multiple Dirichlet priors related by the same
hyperprior. Each
Dirichlet prior can be
independently collapsed and
affects only...
-
Uncertainty about these hyperparameters can, in turn, be
expressed as
hyperprior probability distributions. For example, if one uses a beta distribution...
-
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...
-
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...
-
Hyperparameter (Bayesian statistics)
Hyperparameter (machine learning)
Hyperprior Hypoexponential distribution Idealised po****tion
Idempotent matrix Identifiability...
