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\title{\sffamily{\emph{revrand}: Technical Report}}
\author{Daniel Steinberg, Louis Tiao, Dave Cole\\ 
    Data61 $|$ CSIRO \\
    email: \texttt{\{firstname.lastname\}@data61.csiro.au}
}
\date{}

\begin{document}

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\begin{abstract}
    This is a technical report on the \emph{revrand} software library. This
    library implements various Bayesian linear models (Bayesian linear
    regression), approximate Gaussian processes and generalised linear models.
    These algorithms have been implemented such that they can be used for
    large-scale inference by using stochastic gradients, and are deployable on
    computational clusters with Apache Spark. All of the algorithms in
    \emph{revrand} use a unified feature composition framework, that allows for
    easy composition of positive-definite covariance functions and easy
    manipulation and combination of regression basis functions.
\end{abstract}

\tableofcontents

\section{Introduction}

\section{Core Algorithms}

\subsection{Stochastic Gradients}

\subsection{Variational Objective Functions}

\subsection{Bayesian Linear Regression}

\subsection{Bayesian Generalised Linear Models}

\subsection{Large Scale Gaussian Process Approximation}

\section{Feature Composition Framework}

\section{Apache Spark Bindings}

\section{Future Functionality}

\end{document}
