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

    Textbooks are not required for this paper.

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    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


    Not offered in 2022

    Teaching method
    This paper is taught On Campus
    Learning management system
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