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Description
Nando de Freitas is Associate professor in the
Department of Computer Science at the University of British Columbia .
The first part of his course will consist of two presentations. In the first
presentation, he will introduce fundamentals of Monte Carlo simulation
for statistical inference, with emphasis on algorithms such as
importance sampling, particle filtering and smoothing for dynamic
models, Markov chain Monte Carlo, Gibbs and Metropolis-Hastings,
blocking and mixtures of MCMC kernels, Monte Carlo EM, sequential Monte
Carlo for static models, auxiliary variable methods (Swedsen-Wang,
hybrid Monte Carlo and slice sampling), and adaptive MCMC. The
algorithms will be illustrated with several examples: image tracking,
robotics, image annotation, probabilistic graphical models, and music
analysis.
The second presentation will target model selection and decision making
problems. He will describe the reversible-jump MCMC algorithm and
illustrate it with application to simple mixture models and nonlinear
regression with an unknown number of basis functions. He will show how to
apply this algorithm to general Markov decision processes (MDPs). The course
will also cover other Monte Carlo simulation methods for partially
observed Markov decision processes (POMDPs) using policy gradients,
common random number generation, and active exploration with Gaussian
processes. An outline to some applications of these methods to robotics
and the design of computer game architectures will be given. The
presentation will end with the problem of Monte Carlo simulation for Bayesian
nonlinear experimental design, with application to financial modeling,
robot exploration, drug treatments, dynamic sensor networks, optimal
measurement and active vision.
Lectures: