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|
|Teaching period||Not offered in 2020|
|Domestic Tuition Fees (NZD)||$666.57|
|International Tuition Fees (NZD)||$2,895.09|
- 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,
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- 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