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<title>Computer Vision - University of Leeds - Dima Damen</title>
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This tutorial was held within the Maths Club.<br/>
Topics covered
<li>Monte Carlo Integration</li>
<li>Markov Chain</li>
<li>Markov Chain Monte Carlo Sampling</li>
<li>Metropolis-Hastings Algorithm</li>
<li>Gibbs Sampling</li>
<li>Reversible Jump Markov Chain Monte Carlo</li>
<li>Simulated MCMC</li>

<a href="MCMCTutorial.pps">Tutorial slides: pps</a>
<hr/>
<b>supplementary material</b>
<li>Perron-Frobenius Theorem - <a href="PerronFrobenius.m">Matlab Code</a> <a href="PFReadMe.txt">ReadMe</a></li>
<li>MCMC Demo - <a href="MCMC.m">Matlab Code</a> <a href="MCMCReadMe.txt">ReadMe</a></li>
<hr/>
<b>references</b>
<li>Andrieu, C., N. de Freitas, et al. (2003). An introduction to MCMC for machine learning. <u>Machine Learning</u> 50: 5-43</li>
<li>Zhu, Dalleart and Tu (2005). <u>Tutorial: Markov Chain Monte Carlo for Computer Vision.</u> Int. Conf on Computer Vision (ICCV)
<a href="http://civs.stat.ucla.edu/MCMC/MCMC_tutorial.htm">http://civs.stat.ucla.edu/MCMC/MCMC_tutorial.htm</a></li>
<li>Chib, S. and E. Greenberg (1995). Understanding the Metropolis-Hastings Algorithm. <u>The American Statistician</u> 49(4): 327-335.</li>
<li>Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. <u>Biometrika</u> 57(1): 97-109.
<li>Smith, K. (2007). <u>Bayesian Methods for Visual Multi-object Tracking with Applications to Human Activity Recognition.</u> Ecole Polytechnique Federale de Lausanne (EPFL). PhD: 272</li>
<li>Green, P. (2003). Trans-dimensional Markov chain Monte Carlo. <u>Highly structured stochastic systems.</u> P. Green, N. Lid Hjort and S. Richardson. Oxford, Oxford University Press.</li>
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