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This module equips students with a solid foundation in Bayesian inference for single parameter models, emphasizing both theoretical understanding and practical application.

This module provides an overview of Bayesian inference for multiparameter models, focusing on handling normal data, employing conjugate priors, and applying multivariate normal models to practical scenarios.

This module provides an understanding of large-sample inference and frequency properties in Bayesian analysis, focusing on normal approximations, large-sample theory, and the evaluation of Bayesian methods from a frequentist perspective.

This module provides an overview of hierarchical models within Bayesian inference, focusing on constructing priors, understanding exchangeability, performing analysis, and ensuring model validity and improvement.

This module provides a comprehensive understanding of Bayesian computation techniques, emphasizing numerical integration, simulation methods, and advanced Markov chain algorithms. Students will gain practical skills in implementing these methods and debugging computational issues.

This module consists of an overview of regression models in Bayesian inference, focusing on foundational principles, hierarchical linear models, and generalized linear models, with practical applications and advanced techniques.

This module covers advanced topics in Bayesian inference, focusing on the setup, interpretation, and application of mixture models, as well as addressing computational challenges and integrating mixture models with multivariate data analysis.

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.