beta_priors | R Documentation |
This function allows the user to specify custom values for Gaussian priors on the slope parameters.
beta_priors( k, beta_mean_prior = matrix(0, k, 1), beta_var_prior = diag(k) * 100 )
k |
The total number of slope parameters in the model. |
beta_mean_prior |
numeric k by 1 matrix of prior means \underline{μ}_β. |
beta_var_prior |
A k by k matrix of prior variances \underline{V}_β. Defaults to a
diagonal matrix with |
For the slope parameters β the package uses common Normal prior specifications. Specifically, p(β)\sim\mathcal{N}(\underline{μ}_β,\underline{V}_β).
This function allows the user to specify custom values for the prior hyperparameters \underline{μ}_β and \underline{V}_β. The default values correspond to weakly informative Gaussian priors with mean zero and a diagonal prior variance-covariance matrix with 100 on the main diagonal.
A list with the prior mean vector (beta_mean_prior
), the prior variance matrix
(beta_var_prior
) and the inverse of the prior variance matrix (beta_var_prior_inv
).
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