# prior.pb: Prior parameter distribution for the Pairwise Beta model In lbelzile/BMAmevt: Multivariate Extremes: Bayesian Estimation of the Spectral Measure

## Description

Density and generating function of the prior distribution.

## Usage

 `1` ``` prior.pb(type = c("r", "d"), n, par, Hpar, log, dimData) ```

## Arguments

 `type` One of the character strings `"r"`, `"d"` `n` The number of parameters to be generated. Only used if `type == "r"`. `par` A vector with positive components: The parameter where the density is to be taken. Only used if `type=="d"`. In the Pairwise Beta model, `par` is of length `choose(p,2)+1`. The first element is the global dependence parameter, the subsequent ones are the pairwise dependence parameters, in lexicographic order (e.g. β_{1,2}, β_{1,3}, β_{2,3}. `Hpar` list of Hyper-parameters : see `pb.Hpar` for a template. `log` logical. Should the density be returned on the log scale ? Only used if `type=="d"` `dimData` The dimension of the sample space. (one more than the dimension of the simplex)

## Details

The parameters components are independent, log-normal.

## Value

Either a matrix with `n` rows containing a random parameter sample generated under the prior (if type == "d"), or the (log)-density of the parameter `par`.

Anne Sabourin

## Examples

 ```1 2``` ```## Not run: prior.pb(type="r", n=5 ,Hpar=get("pb.Hpar"), dimData=3 ) ## Not run: prior.pb(type="d", par=rep(1,choose(4,2), Hpar=get("pb.Hpar"), dimData=4 ) ```

lbelzile/BMAmevt documentation built on May 17, 2018, 12:16 p.m.