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)
| 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 |
a named list
Other functions related to the inverse-gamma distribution:
dinvgamma_stanlike(),
plot_invgamma()
# 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 #>