sv_prior: Prior Distributions in 'stochvol'

In stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models

Prior Distributions in stochvol

Description

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.

Usage

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()

Arguments

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

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).

See Also

Other priors: specify_priors()

stochvol documentation built on Nov. 5, 2025, 6:02 p.m.