Prior definitions

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
#>