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. colin.fox 'at' otago.ac.nz

*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

Resources:

*Lecture 1:*Problem and Goals- ELEC 445 Inverse Problems course notes
- Gareth Roberts' notes on Statistical Inference

*Lecture 2:*Evaluating Expectations, MH MCMC proposals in few dimensions*Lecture 3:*Adaptive MCMCs and Efficiency- IA2RMS: Martino Read Luengo 2015 paper and Matlab package
- AM: Roberts and Rosenthal 2009 paper and Marko Laine's MCMC toolbox
- Ulli Wollf's UWerr MatLab code and documentation

*Lecture 4:*Inverse Diffusion Problem*Lecture 5:*Image Models*Lecture 6:*Linear-Gaussian Inverse Problems*Lecture 7:*Approximations*Lecture 8:*Counting objects*Assignment 1:*- Problem sheet
- Matlab code for diffusion problem: Main.m heat.m
- Useful code snippets: tauvsd.m (an example of running AM on multivariate normals), stdnorm_example.m (an example of using IA2RMS to sample from N(0,1)), mcgaus.m (RWM for N(0,1)).

*Assignment 2:*- Problem sheet
- Matlab code for deblurring jupiter image: jupiter1.tif jupiter.m deconv1.m

*Assignment 3:*- Problem sheet
- Image of good and bad cells in Matlab mat format slide.mat, or as slide.tif. Matlab code makefake.m that generated the image, and uses functions putgood.m and putbad.m.

*Project:*

Links:

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

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Page last modified on July 24, 2019, at 09:45 PM