student: Multivariate Student's t Prior Distribution for Model...

In ICAOD: Optimal Designs for Nonlinear Models via ICA

Multivariate Student's t Prior Distribution for Model Parameters

Description

Creates the prior distribution for the parameters as an object of class cprior.

Usage

student(mean, S, df, lower, upper)

Arguments

mean	A vector of length d=ncol(S), representing the location parameter (equal to the mean vector when df>1). For more details, see 'Arguments' in dmt.
S	A symmetric positive-definite matrix representing the scale matrix of the distribution, such that S*df/(df-2) is the variance-covariance matrix when df>2. For more details, see 'Arguments' in dmt.
df	Degrees of freedom; it must be a positive integer. For more details, see 'Arguments' in dmt.
lower	A vector of lower bounds for the model parameters.
upper	A vector of upper bounds for the model parameters.

Value

An object of class cprior that is a list with the following components:

fn

Prior distribution as an R function with argument param, which is the vector of the unknown parameters. See below.

npar

Number of unknown parameters (equal to the length of param).

lower

Lower bounds. A vector with the same length as param.

upper

Upper bounds. A vector with the same length as param.

The list will be passed to the argument prior of the function bayes. The order of the argument param in fn has the same order as the argument parvars when the model is specified by a formula. Otherwise, it is equal to the argument param in the function fimfunc.

See Also

bayes sensbayes

Examples

skewnormal(xi = c(0, 1),
 Omega = matrix(c(1, -0.17, -0.17, .5), nrow = 2),
  alpha = c(1, 0), lower =  c(-3, .1), upper = c(3, 2))

ICAOD documentation built on Feb. 3, 2026, 5:07 p.m.