|Fees||Look up fees|
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, comparisons of means and proportions, multi-model inference and model selection. The second part of the subject focuses on advanced supervised and unsupervised Bayesian machine learning methods in the context of Gaussian processes and Dirichlet processes. The subject will also cover practical implementations of Bayesian methods through Markov Chain Monte Carlo computing and real data applications.
Intended learning outcomes
LO1 A deep understanding of selected advanced topics in Bayesian statistics.
LO2 Development of the mathematical and computational skills needed for further research or applied work in statistics and data science.
LO3 Preparation for a research or industry career in statistics and data science.
LO4 Familiarity with several major texts in Bayesian statistics.
- 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: 2 December 2019