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ELEC 446: Computational Inference

ELEC 446 develops Computational Inference: Bayesian inference for uncertainty quantification, stochastic modelling and MCMC sampling, advanced Monte Carlo strategies, imaging and machine vision.

Semester: 2

Recommended Preparation: Inverse Problems and Imaging module

Lecturer: Colin Fox. Room 503. Ph 4797806. 'at'

Assessment: 30% assignments, 70% Project (ELEC 446 will be internally assessed in 2016)

Lectures and Tutorials: There is a 2-hour lecture and 1-hour tutorial per week



Module 412 in 2015, BUC5 compute resources, Blockkurs on Markov Chain Monte Carlo for Inverse Problems in PDEs

Department of Physics 400-level information

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