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The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in Science and Engineering.
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Description
In this course, we study 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.
Course content
- Quantifying Uncertainty Introduction Unlimited
- In A Linear World Unlimited
- Ensemble Filter Smoother Unlimited
- Exponential Family Unlimited
- Expectation Maximization and Model Selection Unlimited
- Markov Chain Monte Carlo Unlimited
- Hierarchical Bayesian Models Unlimited
- Particle Filter Unlimited
- Graphical Models Unlimited
Instructor
Massachusetts Institute of Technology
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