Undergraduate Programme and Module Handbook 2022-2023 (archived)
Module MATH4341: Spatio-Temporal Statistics
Department: Mathematical Sciences
MATH4341: Spatio-Temporal Statistics
| Type | Open | Level | 4 | Credits | 20 | Availability | Not available in 2022/23 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- Advanced Statistical Modelling (MATH3411) OR Bayesian Computation and Modelling (MATH3421)
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce the theory, computation and practice of the statistical analysis of problems involving aspects of space and time
Content
- Spatial statistics: Spatial point processes; Spatial regression; Covariance and correlation functions; Gaussian processes.
- Temporal statistics and time series: Components and properties of time series; Local and moving-average methods; Forecasting and inference; State-space models.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students will:
- Be aware of a wide range of applicable statistical methodology for spatio-temporal problems.
- Be able to construct statistical models for data with spatial and/or temporal properties
- Have a systematic and coherent understanding of the theory, computation and application of the mathematics underlying the statistical topics and models studied.
- Have acquired a coherent body of applicable knowledge on spatial and temporal statistics.
Subject-specific Skills:
- Students will have specialised statistical skills in the following areas which can be used with minimal guidance: Modelling, Computation.
Key Skills:
- Students will be able to study independently to further their knowledge of advanced statistical modelling.
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
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 42 | 2 per week for 21 weeks | 1 hour | 42 | |
| Problem classes | 8 | Four in each of terms 1 and 2 | 1 hour | 8 | |
| Preparation and reading | 150 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written Examination | 3 hours | 100% | |
Formative Assessment:
• Eight written or electronic assignments to be assessed and returned. • Other assignments are set for self-study and complete solutions are made available to students.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University