Prior Distributions in stochvol
The functions below can be supplied to specify_priors
to overwrite the default set of prior distributions in svsample.
The functions have mean, range, density, and
print methods.
sv_constant(value) sv_normal(mean = 0, sd = 1) sv_multinormal(mean = 0, precision = NULL, sd = 1, dim = NA) sv_gamma(shape, rate) sv_inverse_gamma(shape, scale) sv_beta(shape1, shape2) sv_exponential(rate) sv_infinity()
value |
The constant value for the degenerate constant distribution |
mean |
Expected value for the univariate normal distribution or mean vector of the multivariate normal distribution |
sd |
Standard deviation for the univariate normal distribution or constant scale of the multivariate normal distribution |
precision |
Precision matrix for the multivariate normal distribution |
dim |
(optional) Dimension of the multivariate distribution |
shape |
Shape parameter for the distribution |
rate |
Rate parameter for the distribution |
scale |
Scale parameter for the distribution |
shape1 |
First shape parameter for the distribution |
shape2 |
Second shape parameter for the distribution |
Multivariate normal objects can be specified several ways. The most general way is by calling
sv_multinormal(mean, precision), which allows for arbitrary mean and (valid) precision
arguments. Constant mean vectors and constant diagonal precision matrices of dimension D
can be created two ways: either sv_multinormal(mean, sd, dim = D) or
rep(sv_normal(mean, sd), length.out = D).
Other priors:
specify_priors()
Other priors:
specify_priors()
Other priors:
specify_priors()
Other priors:
specify_priors()
Other priors:
specify_priors()
Other priors:
specify_priors()
Other priors:
specify_priors()
Other priors:
specify_priors()
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