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Specify normal/exponential priors for MAP and Bayesian inference

Source: R/wrappers_prior.R
prior_normal.Rd

`prior_normal()` specifies normal priors for location parameters and exponential priors for scale parameters. It is intended for MAP and Bayesian inference, not for `classic()`.

Usage

prior_normal(
  Intercept_sd = 10,
  mu_sd = 10,
  b_sd = 10,
  sigma_rate = 5,
  tau_rate = 5,
  ...
)

Arguments

Intercept_sd

Standard deviation for the intercept prior. If `NULL`, no intercept prior is added.

mu_sd

Standard deviation for mean/intercept priors. If `NULL`, no mean prior is added.

b_sd

Standard deviation for coefficient priors. If `NULL`, no coefficient prior is added.

sigma_rate

Rate for residual standard deviation priors. If `NULL`, no sigma prior is added.

tau_rate

Rate for random-effect standard deviation priors. If `NULL`, no tau prior is added.

...

Optional wrapper-specific hyperparameters.

Value

A list with class `"rtmb_prior"` and `type = "normal"`.