mposterior: Maximizing Posterior Distributions

In evdbayes: Bayesian Analysis in Extreme Value Theory

Maximizing Posterior Distributions

Description

Maximizing prior and posterior distibutions for the location (with optional trend), scale and shape parameters under the gev, order statistics or point process models.

Usage

mposterior(init, prior, lh = c("none", "gev", "gpd", "pp", "os"),
    method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN"),
    lower = -Inf, upper = Inf, control = list(), hessian = FALSE, ...)

Arguments

init	Numeric vector of length three/four, giving the initial values for the optimization.
prior	An object of class "evprior", constructed using prior.prob, prior.quant or prior.norm.
lh	A character string specifying the likelihood; either "gev" for gev, "gpd" for gpd, "os" for order statistics, "pp" for Poisson process or "none" for none (the default). The latter can be used to maximize the prior distribution.
method	The method to be used. See optim.
lower, upper	Bounds on the variables for the "L-BFGS-B" method. See optim.
control	A list of control parameters. See optim.
hessian	Logical. See optim.
...	Arguments to the likelihood. Should include data unless lh is "none". Should also include thresh and noy if lh is "pp". Should include the vector trend if a linear trend on the location is implemented. See pplik for details.

Value

A list. See optim.

See Also

pplik, posterior, prior.prob

evdbayes documentation built on March 7, 2023, 5:34 p.m.