BNormalVar {Baldur}R Documentation

Bayesian Normal Variance Model

Description

BNormalVar produces iid draws from the posterior density of a Bayesian Normal Variance model with a known mean and a inverse gamma prior for the data variance.

Usage

 BNormalVar(y, mu, Var0, n0, draws)

Arguments

y n*1 Matrix with observed data points
mu prior mean
Var0 Prior point estimate for data variance
n0 n0 number of prior observations for data variance
draws Number of desired draws from the posterior density

Details

Makes use of the likelihood subgradient density accept/reject procedure of Nygren and Nygren (2006) in order to generate iid samples from the posterior density of a Poisson regression model with a multivariate normal prior. If the posterior density is close to multivariate normal, then the expected number of draws should be approximately equal to $(2/sqrt{pi})^{k}$.

Value

If A=FALSE, a matrix beta. If A=TRUE, a list containing the matrix beta and a matrix Accept.:

beta draws*k matrix with the iid draws for the model parameters.
Accept draws*1 matrix with the number of candidates for each draw that were required before acceptance in the accept/reject procedure.

Author(s)

Kjell Nygren knygren@us.imshealth.com

Examples


# Example: Bayesian Poisson regression model for Seizure Data 

# Step 1: Read in and setup data for BPoisson Function

data( Seizure,package = "Baldur")

y<-as.matrix(Seizure[,1])
X<-as.matrix(Seizure[,2:7])

mu<-matrix(0,nrow=6,ncol=1)
alpha<-matrix(0,nrow=236,ncol=1)
P<-100*diag(6)
draws<-1000

# Step 2: Set random number seed and run simulation for Bayesian Poisson Regression

set.seed(666)
system.time(sim<-BPoisson(y,X,alpha,mu,P,draws))
[Package Baldur version 0.0-0 Index]