lgpr (version 1.2.4)

priors: Prior definitions

Description

These use the same parametrizations as defined in the 'Stan' documentation. See the docs for gamma and inverse gamma distributions.

Usage

uniform(square = FALSE)
normal(mu, sigma, square = FALSE)
student_t(nu, square = FALSE)
gam(shape, inv_scale, square = FALSE)
igam(shape, scale, square = FALSE)
log_normal(mu, sigma, square = FALSE)
bet(a, b)

Value

a named list

Arguments

square

is prior for a square-transformed parameter?

mu

mean

sigma

standard deviation

nu

degrees of freedom

shape

shape parameter (alpha)

inv_scale

inverse scale parameter (beta)

scale

scale parameter (beta)

a

shape parameter

b

shape parameter

See Also

Other functions related to the inverse-gamma distribution: dinvgamma_stanlike(), plot_invgamma()

Examples

# Log-normal prior
log_normal(mu = 1, sigma = 1)

# Cauchy prior
student_t(nu = 1)

# Exponential prior with rate = 0.1
gam(shape = 1, inv_scale = 0.1)

# Create a similar priors as in LonGP (Cheng et al., 2019)
# Not recommended, because a lengthscale close to 0 is possible.
a <- log(1) - log(0.1)
log_normal(mu = 0, sigma = a / 2) # for continuous lengthscale
student_t(nu = 4) # for interaction lengthscale
igam(shape = 0.5, scale = 0.005, square = TRUE) # for sigma