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\name{mnlHess}
\alias{mnlHess}
\concept{multinomial logit}
\concept{hessian}
\title{ Computes --Expected Hessian for Multinomial Logit}
\description{\code{mnlHess} computes expected Hessian (\eqn{E[H]}) for Multinomial Logit Model.}
\usage{mnlHess(beta, y, X)}
\arguments{
\item{beta}{ \eqn{k x 1} vector of coefficients }
\item{y}{ \eqn{n x 1} vector of choices, (\eqn{1,\ldots,p}) }
\item{X}{ \eqn{n*p x k} Design matrix }
}
\details{
See \code{\link{llmnl}} for information on structure of X array. Use \code{\link{createX}} to make X.
}
\value{\eqn{k x k} matrix}
\section{Warning}{
This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type.
}
\author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.}
\references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. }
\seealso{ \code{\link{llmnl}}, \code{\link{createX}}, \code{\link{rmnlIndepMetrop}} }
\examples{
\dontrun{mnlHess(beta, y, X)}
}
\keyword{models}
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