priors: Prior definitions

In jtimonen/lgpr: Longitudinal Gaussian Process Regression

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)

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

Value

a named list

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

jtimonen/lgpr documentation built on Oct. 12, 2023, 11:13 p.m.