# update: Applying Bayes Rule In imPois: Imprecise Inference for Poisson Sampling Models

## Description

The Bayes rule is applied to an imprecise prior and produce an imprecise posterior.

## Usage

 ```1 2 3 4``` ```## S3 method for class 'impinf' update(object, y = NULL, wrt = c("canonical", "mean"), ...) update2.impinf(object, y = NULL, ...) ```

## Arguments

 `object` an object for which an update is needed `y` vector of observations `wrt` parameterization method with respect to canonical or mean `...` further arguments passed to methods

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# 2-dimensions lc0 <- list(lhs=rbind(diag(2), -diag(2)), rhs=c(0,0,-1,-1)) op <- iprior(ui=rbind(diag(2), -diag(2)), ci=c(0,0,-1,-1)) op <- iprior(ui=rbind(c(1,0),c(0,1),c(-1,-1)), ci=c(0,0,-5)) op <- iprior(ui=rbind(c(1,0),c(0,1),c(0,-1),c(1,1),c(-2,-1)), ci=c(1,2,-8,5,-14)) # (3,8),(1,8), (1,4),(3,2)(6,2) op1 <- update(op, y=NULL) # 3-dimensions lc0 <- rbind(c(1,2,0), c(1,-2,0), c(0.5,-2,0), c(0.5,2,0)) op <- iprior(pmat=lc0) op1 <- update(op, y=NULL) ```

imPois documentation built on May 30, 2017, 3:32 a.m.