PAOC Spotlights

PAOC Spring Course 12.S990

Mon January 30th, 2012

Photo credit: Wikipedia

The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in science and engineering. In this course, Ravela will lead a study of advanced methods to represent, sample, update and propagate uncertainty.

This is a "hands on" course: Methodology will be coupled with applications. The course will include lectures, invited talks, discussions, reviews and projects and will meet once a week to discuss a method and its applications.

Methods under consideration will include Markov Chain Monte Carlo, Polynomial Chaos, Spectral and Scale-space Model Reduction, Expectation Maximization, Hierarchical and Variational Bayes, Particle Filter and Smoother, Graphical Models, Spatial Inference, Principal Modes - EOF (PCA), ICA, NNMF, UCA..., Reconstruction and Inverse Problems with L1, L2... L-Infinity or mutual information measures, under entropy, smoothness, sparsity or compressibility.

Applications to include: Autonomous Observation, Climate Reconstruction, Data Assimilation/ State Estimation, Image Processing, Natural Hazards and Predictability.

Instructor: Sai Ravela


First class: Friday, February 10th in 54-1623 @10am.

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