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MATH4317: Robust Bayesian Analysis IV

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Type Open
Level 4
Credits 10
Availability Not available in 2023/24
Module Cap None.
Location Durham
Department Mathematical Sciences

Prerequisites

  • Statistical Inference (MATH2711)

Corequisites

  • None

Excluded Combinations of Modules

  • None

Aims

  • To provide advanced methodological and practical knowledge in the field of Bayesian statistics, specifically robust Bayesian methods and the foundational aspects and ramifications of various Bayesian paradigms.

Content

  • Introduction to Robust Bayesian Analysis.
  • Bergers critique of p-values.
  • Prior / likelihood sensitivity analysis.
  • Model mis-specification.
  • Robust Decisions.
  • Foundations of Bayesian statistics.
  • Bayes linear methods.
  • Seminar classes on interpretations of probability and extensions.

Learning Outcomes

Subject-specific Knowledge:

  • By the end of the module students will:
  • develop an understanding of the importance of robustness considerations in Bayesian statistical analyses,
  • be able to elevate a typical Bayesian analysis into a robust Bayesian form, and use the acquired skills to explore its robustness,
  • have a systematic and coherent understanding of the foundational theory and mathematics underlying various Bayesian paradigms, and their strengths and weaknesses,
  • have acquired a coherent body of knowledge regarding Bayes linear methods,
  • understand how the conceptual framework of Bayes linear methodology relates to practical implementation to real world problems.

Subject-specific Skills:

  • Students will have advanced mathematical skills in the following areas: robust Bayesian inference and the foundations of Bayesian statistics.

Key Skills:

  • Students will have advanced skills in the following areas: problem formulation and solution, critical and analytical thinking.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
  • Problem classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
  • Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
  • Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
  • The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
Lectures212 per week in weeks 1-10, one in week 211 hour21 
Problem classes4One in weeks 4, 6, 8, 101 hour4 
Preparation and reading75 
Total100 

Summative Assessment

Component: ExaminationComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Written Examination2 hours100 

Formative Assessment

Four written or electronic assignments to be assessed and returned.Other assignments are set for self-study and complete solutions are made available to students.

More information

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