# BAYESIAN STATISTICS - 2017/8

Module code: MAT3003

Module provider

Mathematics

Module Leader

SANTITISSADEEKORN N Dr (Maths)

Number of Credits

15

ECTS Credits

7.5

Framework

FHEQ Level 6

JACs code

G300

Module cap (Maximum number of students)

N/A

Module Availability

Semester 2

Overall student workload

Independent Study Hours: 117

Lecture Hours: 27

Tutorial Hours: 6

Assessment pattern

Assessment type | Unit of assessment | Weighting |
---|---|---|

Examination | EXAMINATION - 2 HOURS | 80 |

School-timetabled exam/test | CLASS TEST (50 MINS) | 20 |

Alternative Assessment

N/A

Prerequisites / Co-requisites

MAT2013 Mathematical Statistics

Module overview

The module looks at the branch of statistics called Bayesian Statistics. It relies on subjective probability and looks at why this is extremely useful for modelling realistic problems. The module covers an introduction to Bayesian statistics, incorporating prior to posterior analysis for a wide range of statistical models. This shows the students an alternative approach to the Classical statistics that they have studied so far and looks at various statistical techniques that they have studied before and gives them a Bayesian approach.

Module aims

introduce the rationale for, the main techniques of, and general issues in Bayesian statistics

apply techniques to standard statistical models, including exponential families and linear models

apply Bayesian approaches to estimation and testing

introduce Bayesian prediction

consider the role of decision theory

Learning outcomes

Attributes Developed | ||
---|---|---|

1 | Analyse the differences between the Bayesian paradigm and frequentist statistical methods | KC |

2 | Calculate the posterior and predictive distribution and related quantities | KC |

3 | Define hierarchical models and state and prove related theorems | KC |

4 | Demonstrate how models can be written in hierarchical form and calculate posterior quantities | KCPT |

5 | Explain the arguments for and against the Bayesian paradigm. | KC |

Attributes Developed

**C** - Cognitive/analytical

**K** - Subject knowledge

**T** - Transferable skills

**P** - Professional/Practical skills

Module content

Indicative content includes:

review of distribution theory

subjective probability and prior distributions – noninformative and conjugate

prior to posterior analysis

exponential families, sufficiency and conjugate priors

predictive inference

Bayesian estimation and hypothesis testing

application to linear models

approximate methods to estimation

elements of decision theory and comparative inference

Methods of Teaching / Learning

The learning and teaching strategy is designed to provide:

A detailed introduction to the theory behind, methodology and approaches used in Bayesian statistics

Experience (through demonstration) of the methods used to interpret, understand and solve problems in analysis

The learning and teaching methods include:

3 x 1 hour lectures per week x 11 weeks, with additional notes on white board to supplement the module handbook and Q + A opportunities for students.

(every second week) 1 x 1 hour tutorial replaces one of the lectures for guided discussion of solutions to problem sheets provided to and worked on by students during the tutorial.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

· Understanding of and ability to interpret and manipulate mathematical statements.

· Subject knowledge through the recall of key definitions, theorems and their proofs.

· Analytical ability through the solution of unseen problems in the test and exam.

Thus, the summative assessment for this module consists of:

· One two hour examination at the end of Semester 2; worth 75% module mark.

· One 50 minute in-semester test; worth 25% module mark.

Formative assessment and feedback

Students receive written feedback via a number of marked coursework assignments over an 11 week period. In addition, verbal feedback is provided by lecturer at biweekly tutorial lectures.

Reading list

Reading list for BAYESIAN STATISTICS : http://aspire.surrey.ac.uk/modules/mat3003

Programmes this module appears in

Programme | Semester | Classification | Qualifying conditions |
---|---|---|---|

Economics and Mathematics BSc (Hons) | 2 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Financial Mathematics BSc (Hons) | 2 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics with Music BSc (Hons) | 2 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics with Statistics BSc (Hons) | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |

Mathematics BSc (Hons) | 2 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics MMath | 2 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics and Computer Science BSc (Hons) | 2 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.