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ELEC446 Computational Inference

NOT OFFERED IN 2018

Paper Description

Advanced stochastic modelling and Monte Carlo strategies for implementing Bayesian inference with low-level, mid-level and high-level representations, for estimation and prediction in physically-based inverse problems.

Bayesian inference can be described as the mathematics of making up your mind. This paper covers contemporary Monte Carlo methods for performing computational inference for inverse problems, and tools for representation of unknowns. Aimed at students who will go on to analyze data from complex physical systems, or who would like to compute with states of knowledge.

Prerequisites:
None

Recommended:
ELEC445

This paper consists of 8 lectures and 8 workshops. There are 3 assignments.

Assesment:
Final Exam 70%, Assignments 30%

Important information about assessment for ELEC446

Course Coordinator:
Assoc Prof Colin Fox

After completing this paper students are expected to have achieved the following major learning objectives:

  • 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 a 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

Additional outcomes:
An overall goal is to broaden each student's horizons about the possibilities of computing with states of knowledge, and the interplay between knowledge and representation.

Topics:

  • Expectations, univariate sampling
  • MCMC basics
  • Algorithmic efficiency
  • Inverse diffusion problem
  • Image reconstruction
  • Linear-Gaussian problems
  • Counting objects

The ELEC446 Support Home Page.

 


Formal University Information

The following information is from the University’s corporate web site.

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Details

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 2019
Domestic Tuition Fees (NZD) $653.49
International Tuition Fees (NZD) $2,757.23

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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:
  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

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Timetable

Not offered in 2019

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None

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 2020
Domestic Tuition Fees Tuition Fees for 2020 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

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:
  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

Timetable

Not offered in 2020

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
None