Module 412: Computational Inference
Module 412 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% Exam
Resources:
- Lecture 1: Problem and Goals
- Lecture 2: Expectations
- Adaptive rejection paper by Gilks Tan Best
- Lecture 3: MCMC basics
- Lecture 4: Algorithmic Efficiency
- Lecture 5: Inverse Diffusion Problem
- Lecture 6: Image Reconstruction
- Lecture 7: Linear/Gaussian problems
- Lecture 8: Counting objects
- Assignment 1:
- Assignment 2:
- Assignment 3:
Links:
Department of Physics 400-level information