\documentclass[addpoints,11pt]{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{}{}{{\bf Supervisor:} Fabian Sinz} \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 which naturally should yield maximal information)? \part Use bootstrapping to compute the $95\%$ confidence interval for the mutual information estimate in that dataset. \end{parts} \end{questions} \end{document}