Overview
Advanced stochastic modelling and Monte Carlo strategies for implementing Bayesian inference with low-level, mid-level and high-level representations, aimed at estimation and prediction in physically-based inverse problems.
About this paper
| Paper title | Computational Inference |
|---|---|
| Subject | Electronics |
| EFTS | 0.0833 |
| Points | 10 points |
| Teaching period | Not offered in 2022 (On campus) |
| Domestic Tuition Fees ( NZD ) | $685.39 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- ELEC 445
- Limited to
- BSc(Hons), PGDipSci, MSc, MAppSc
- Contact
- colin.fox@otago.ac.nz
- Teaching staff
- Assoc Prof Colin Fox
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Research, Self-motivation, Teamwork.
View more information about Otago's graduate attributes. - Learning Outcomes
- After completing this paper students are expected to:
- Build stochastic models over low-level, mid-level and high-level representations
- Know the basic methods of statistical inference in the Bayesian framework
- State a correct Markov chain Monte Carlo algorithm for a range of state spaces and be able to prove distributional convergence of that algorithm
- Know how to define and evaluate computational efficiency of an MCMC
- Be able to solve inverse problems in simple PDEs, for linear forward maps and in image reconstruction using a suitable MCMC and be able to present resulting estimates and uncertainties in an accessible graphical form
- Be able to count objects by implementing a high-level representation and quantify uncertainty in number