# Bare bones beamer

#### Hyndsight

Beamer is far and away the most popular software for presentations amongst researchers in mathematics and statistics. Most conference and seminar talks I attend these days use beamer. Unfortunately, they all look much the same. I think people find beamer themes too hard to modify easily, so a small number of templates get shared around. Even the otherwise wonderful LaTeX Templates site has no beamer examples.

The beamer user guide explains how to make changes but it is not for the faint-hearted (although it is a fantastic resource once you have some expertise).

So I thought it might be useful to produce a very simple beamer template that is easy to extend and modify.

This template also includes two example slides showing some of the most common things you might want to do.

\documentclass[14pt,xcolor=dvipsnames]{beamer}

% Specify theme
% See http://goo.gl/Wxlyy for alternative themes

% Specify base color
\usecolortheme[named=OliveGreen]{structure}
% See http://goo.gl/p0Phn for alternative colors

% Specify other colors and options as required
\setbeamertemplate{items}[square]

% Title and author information
\title{Title goes here}
\author{Name goes here}

\begin{document}

\begin{frame}
\titlepage
\end{frame}

\begin{frame}{Outline}
\tableofcontents
\end{frame}

\section{Introduction}

\begin{frame}{Forecasting functional data}
\item Observed values are discrete but underlying structures are
continuous functions.
\item Observed values may be noisy but underlying functions are
smooth.
\item \textbf{Problem:} To forecast the \textbf{whole function} for
future time periods.
\end{itemize}
\end{frame}

\begin{frame}{Forecasting functional data}
\structure{Some notation}

Let $y_t(x_i)$ be the observed data in period $t$ at location $x_i$,
$i=1,\dots,p$, $t=1,\dots,n$.
\pause

\begin{block}{}
$y_t(x_i) = f_t(x_i) + \sigma_t(x_i)\varepsilon_{t,i}$
where $\varepsilon_{t,i}$ is iid N(0,1) and $\sigma_t(x_i)$ allows the
amount of noise to vary with $x$.
\end{block}
\pause

\item We assume $f_t(x)$ is a smooth function of $x$.
\item We need to estimate $f_t(x)$ from the data for $x_1 < x < x_p$.