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

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)
#> $dist #> [1] "log-normal" #> #> $square #> [1] FALSE #> #> $mu #> [1] 1 #> #> $sigma #> [1] 1 #>
# Cauchy prior student_t(nu = 1)
#> $dist #> [1] "student-t" #> #> $square #> [1] FALSE #> #> $nu #> [1] 1 #>
# Exponential prior with rate = 0.1 gam(shape = 1, inv_scale = 0.1)
#> $dist #> [1] "gamma" #> #> $alpha #> [1] 1 #> #> $beta #> [1] 0.1 #> #> $square #> [1] FALSE #>
# 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
#> $dist #> [1] "log-normal" #> #> $square #> [1] FALSE #> #> $mu #> [1] 0 #> #> $sigma #> [1] 1.151293 #>
student_t(nu = 4) # for interaction lengthscale
#> $dist #> [1] "student-t" #> #> $square #> [1] FALSE #> #> $nu #> [1] 4 #>
igam(shape = 0.5, scale = 0.005, square = TRUE) # for sigma
#> $dist #> [1] "inv-gamma" #> #> $alpha #> [1] 0.5 #> #> $beta #> [1] 0.005 #> #> $square #> [1] TRUE #>