Description Usage Arguments Examples
The Bayes rule is applied to an imprecise prior and produce an imprecise posterior.
1 2 3 4 |
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 |
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)
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