ST911 Fundamentals of Modern Statistical Inference
ST911-15 Fundamentals of Modern Statistical Inference
Introductory description
This module runs in Term 1 and is usually taken by PhD students in the Warwick Centre for Doctoral Training in Mathematics and Statistics.
Other PhD students should consult the module leader if they are interested in taking this module.
This module is not available to undergraduate or postgraduate taught students.
Module aims
The aims of this module are to provide a basic introduction to ideas of formal statistical inference for students with a strong mathematical background and undergraduate performance. The course will provide all students with a basic background level of knowledge of inference and, others with a platform to use and research in Statistics within their PhD. The course will reflect the modern trends in Statistical Inference towards powerful computationally intensive methods.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Statistical distribution theory
Methods of inference
Maximum likelihood estimation
Elements of Bayesian Inference
Decision Theory Inference
Basic simulation methodologies
Markov Chain Monte-Carlo methods
Learning outcomes
By the end of the module, students should be able to:
- Understand key ideas of parameter estimation.
- Understand how to apply Statistical tools in various applied problems.
- Develop suitable strategies for the extraction of parameters from data sets.
Indicative reading list
- R. Keener, "Theoretical Statistics"
- M.J. Schervish, "Theory of Statistics"
- J. Shao, "Mathematical Statistics"
- L. Wasserman, "All of Statistics"
View reading list on Talis Aspire
Subject specific skills
TBC
Transferable skills
TBC
Study time
| Type | Required |
|---|---|
| Lectures | 30 sessions of 1 hour (20%) |
| Private study | 120 hours (80%) |
| Total | 150 hours |
Private study description
Weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group C2
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Assessed exercises | 50% | Yes (extension) | |
|
2 x assessed exercises due at evenly spaced intervals during term 1. |
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| Oral examination | 50% | No | |
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The oral examination will take place in the week following the end of term 1. |
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Feedback on assessment
Written feedback will be provided for the problem sets and oral examination within 20 working days.
Courses
This module is Core for:
- Year 1 of RSTA-G4P0 Postgraduate Research Statistics
This module is Core optional for:
- Year 1 of TMAA-G3G2 Postgraduate Taught Mathematics and Statistics
This module is Optional for:
- Year 1 of TMAA-G1PD Postgraduate Taught Interdisciplinary Mathematics (Diploma plus MSc)
- Year 1 of TMAA-G1P0 Postgraduate Taught Mathematics
- Year 1 of TMAA-G1PC Postgraduate Taught Mathematics (Diploma plus MSc)
This module is Option list A for:
- Year 1 of RMAA-G1PG Postgraduate Research Mathematics of Systems
This module is Option list B for:
- Year 1 of TMAA-G1P0 Postgraduate Taught Mathematics
This module is Option list C for:
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TMAA-G1PD Postgraduate Taught Interdisciplinary Mathematics (Diploma plus MSc)
- Year 1 of G1PD Interdisciplinary Mathematics (Diploma plus MSc)
- Year 2 of G1PD Interdisciplinary Mathematics (Diploma plus MSc)
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TMAA-G1PC Postgraduate Taught Mathematics (Diploma plus MSc)
- Year 1 of G1PC Mathematics (Diploma plus MSc)
- Year 2 of G1PC Mathematics (Diploma plus MSc)
| Catalogue |
| Resources |
| Feedback and Evaluation |
| Grade Distribution |
| Timetable |
Assessments dates for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook.