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# This sampler and laplace code was adapted from rwmetrop function in
# LearnBayes package
rwmetrop <- function(par.init, logpost, Data, proposal, R,
grp = rep(1, length(par.init)), burn = 0, thin = 1, report.period = 1000)
{
qq <- length(par.init)
V.proposal.half.trans <- t(proposal$scale * chol(proposal$var))
logf <- function(par) { logpost(par, Data) }
if (is.infinite(logf(par.init)) | is.na(logf(par.init))) {
stop("logpost(par.init) must be a finite value")
}
grp.idx.list <- split(1:qq - 1, grp)
ret <- rwmetrop_cpp(par.init, logf, V.proposal.half.trans,
grp.idx.list, R, burn, thin, report.period)
return(ret)
}
laplace <- function (logpost, mode, Data, optim.control = list(),
optim.method = "L-BFGS-B")
{
optim.control$fnscale <- -1
fit <- optim(mode, logpost, gr = NULL, Data, hessian = TRUE,
method = optim.method, control = optim.control)
mode <- fit$par
H <- -solve(fit$hessian)
p <- length(mode)
int <- p/2 * log(2 * pi) + 0.5 * log(det(H)) + logpost(mode, Data)
list(mode = mode, var = H, int = int, converge = (fit$convergence == 0), optim.out = fit)
}
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