Syllabus: Monte Carlo Methods

Description: Computationally intensive statistical methodologies and their theoretical foundations. Design, implementation and analysis of correct, scalable inference software.

For Term 2 of 2017-2018, the course will focus on Monte Carlo methods, from foundations to recent advances. If time permits, topics at the interface with optimization will be covered.

Relevant topics:

Assessment will consist of implementation, analysis and comparison of various computationally intensive statistical methodologies.

References:

There is no single textbook that I would say covers all the ground unfortunately. I will provide notes which can be your primary source. If you would like to look at some reference textbooks, consider:

Evaluation

Final project:

The course project involves independent work on a topic of your choice, with the constraint that you should make use of some of the theory covered in class, or extension of these techniques. There are three main types of projects: application, methodology, and theory, as described in class. Combinations of these is also encouraged. Extending the exercises is a good way to start thinking about project ideas.

Office hours, TA, piazza, etc: See Contact.

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