Usage
HyperparametersMarginal(k = 0L, mu.0 = 0, tau2.0 = 100, eta.0 = 1, m2.0 = 0.1, alpha, beta = 0.1, a = 1.8, b = 6)
Arguments
k
length-one integer vector specifying number of components
(typically 1
mu.0
length-one numeric vector of the mean for the normal
prior of the component means
tau2.0
length-one numeric vector of the variance for the normal
prior of the component means
eta.0
length-one numeric vector of the shape parameter for
the Inverse Gamma prior of the component variances. The shape
parameter is parameterized as 1/2 * eta.0.
m2.0
length-one numeric vector of the rate parameter for
the Inverse Gamma prior of the component variances. The rate
parameter is parameterized as 1/2 * eta.0 * m2.0.
alpha
length-k numeric vector of the shape parameters for
the dirichlet prior on the mixture probabilities
beta
length-one numeric vector for the parameter of the
geometric prior for nu.0 (nu.0 is the shape parameter of the
Inverse Gamma sampling distribution for the component-specific
variances). beta is a probability and must be in the interval
[0,1].
a
length-one numeric vector of the shape parameter for the
Gamma prior used for sigma2.0 (sigma2.0 is the shape parameter of
the Inverse Gamma sampling distribution for the component-specific
variances)
b
a length-one numeric vector of the rate parameter for the
Gamma prior used for sigma2.0 (sigma2.0 is the rate parameter of
the Inverse Gamma sampling distribution for the component-specific
variances)