Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

ELEC446 Computational Inference

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.

Paper title Computational Inference
Paper code ELEC446
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.

^ Top of page

ELEC 445
Limited to
BSc(Hons), PGDipSci, MSc, MAppSc
Teaching staff
Assoc Prof Colin Fox

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:
  1. Build stochastic models over low-level, mid-level and high-level representations
  2. Know the basic methods of statistical inference in the Bayesian framework
  3. State a correct Markov chain Monte Carlo algorithm for a range of state spaces and be able to prove distributional convergence of that algorithm
  4. Know how to define and evaluate computational efficiency of an MCMC
  5. 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
  6. Be able to count objects by implementing a high-level representation and quantify uncertainty in number

^ Top of page


Not offered in 2022

Teaching method
This paper is taught On Campus
Learning management system