mutual information

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Fabian Sinz 2014-10-31 15:14:52 +01:00
parent e719be714f
commit 2c1d9d93fa
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latex:
pdflatex *.tex > /dev/null
pdflatex *.tex > /dev/null
clean:
rm -rf *.log *.aux *.zip *.out auto
rm -f `basename *.tex .tex`.pdf
zip: latex
zip `basename *.tex .tex`.zip *.pdf *.dat *.mat

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\documentclass[addpoints,10pt]{exam}
\usepackage{url}
\usepackage{color}
\usepackage{hyperref}
\pagestyle{headandfoot}
\runningheadrule
\firstpageheadrule
\firstpageheader{Scientific Computing}{Project Assignment}{11/05/2014
-- 11/06/2014}
%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
\firstpagefooter{}{}{}
\runningfooter{}{}{}
\pointsinmargin
\bracketedpoints
%\printanswers
%\shadedsolutions
\begin{document}
%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
\sffamily
% \begin{flushright}
% \gradetable[h][questions]
% \end{flushright}
\begin{center}
\input{../disclaimer.tex}
\end{center}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\begin{questions}
\question A subject was presented two possible objects for a very
brief time ($50$ms). The task of the subject was to report which of
the two objects was shown. In {\tt decisions.mat} you find an array
that stores which object was presented in each trial and which
object was reported by the subject.
\begin{parts}
\part Plot the data appropriately.
\part Compute a 2-d histogram that shows how often different
combinations of reported and presented came up.
\part Normalize the histogram such that it sums to one (i.e. make
it a probability distribution $P(x,y)$ where $x$ is the presented
object and $y$ is the reported object). Compute the probability
distributions $P(x)$ and $P(y)$ in the same way.
\part Use that probability distribution to compute the mutual
information $$I[x:y] = \sum_{x\in\{1,2\}}\sum_{y\in\{1,2\}} P(x,y)
\log_2\frac{P(x,y)}{P(x)P(y)}$$ that the answers provide about the
actually presented object.
\part What is the maximally achievable mutual information (try to
find out by generating your own dataset; the situation in which
the information is maximal is pretty straightforward)?
\part Use bootstrapping to compute the $95\%$ confidence interval
for the mutual information estimate in that dataset.
\end{parts}
\end{questions}
\end{document}