\documentclass[11pt]{article}

\begin{document}

\author{Philip A. Viton}
\title{A \texttt{HEVEA} Example}
\maketitle

\begin{abstract}
We demonstrate some easy LaTeX-to-HTML conversion using Hevea
\end{abstract}

\section{Introduction}

Let an individual $i$ have a choice set of size $K$, and let $i$'s choices
be discrete. The conditional indirect utility that $i$ achieves if $k\in K$
is selected is 
\[
v_{ik}=\mathbf{X}_{ik}\beta +\epsilon _{ik}
\]
where $\mathbf{X}_{ik}$ is an $n$-vector of attributes of choice $k$ as
perceived by indvidiual $i$, $\beta $ is an $n$-element weighting vector,
and $\epsilon _{ik}$ is a random variable. The individual chooses that
alternative $m$ whose conditional indirect utility is greatest (ie,
maximizes utility). If the $\epsilon _{ik}$ are independent, identically
distributed Type-1-Extreme-Value variates, then we have the alternative-$m$
choice probability $P_{im}$ given by 
\begin{eqnarray*}
P_{im} &=&\Pr [v_{im}>v_{ik,},\forall k\neq m] \\
&=&\left( \frac{e^{\mathbf{X}_{im}\beta }}{\sum_{k}e^{\mathbf{X}_{ik}\beta }}%
\right)
\end{eqnarray*}
the \emph{logit} model.

\end{document}
