Bayesian Statistical Learning (MAST90125)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 2
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Bayesian inference treats all unknowns as random variables, and the core task is to update the probability distribution for each unknown as new data is observed. After introducing Bayes’ Theorem to transform prior probabilities into posterior probabilities, the first part of this subject introduces theory and methodological aspects underlying Bayesian statistical learning including credible regions, prior choice, comparisons of means and proportions, multi-model inference and model selection. The second part of the subject will cover practical implementations of Bayesian methods through Markov Chain Monte Carlo computing and real data applications, focusing on (generalised) linear models and concluding by exploring machine learning techniques such as Gaussian processes.

Intended learning outcomes

After completing this subject students should have:

  • An understanding of selected advanced topics in Bayesian statistics;
  • Developed the mathematical and computational skills needed for further research or applied work in statistics and data science;
  • Preparation for a research or industry career in statistics and data science;
  • Familiarity with several major texts in Bayesian statistics.

Generic skills

  • In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include: - problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies; - analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis; - collaborative skills: the ability to work in a team; - time-management skills: the ability to meet regular deadlines while balancing competing commitments

Last updated: 4 March 2025