Module Descriptor 2012/13
School of Computer Science and Statistics.
Module Code
ST3455
Module Name
Modern Statistical Methods I
Module Short
Title
ECTS
weighting
Semester/term
taught
Contact Hours
N/a
5
Michaelmas
Lecture hours: 36
Lab hours: 12
Tutorial hours: 0
Total hours: 48
Module
Personnel
Learning
Outcomes
Lecturing staff: Cathal Walsh
After this course, students will be able to;
Identify computational approaches to statistical inference.
List the advantages and disadvantages of these strategies.
Describe methods of computing monte carlo estimates.
Explain the theoretical basis for Markov Chain Monte Carlo.
Illustrate how resampling methods are embedded in the classical approaches to
inference.
Module
Learning Aims
This module aims to introduce statistical inferential approaches by means of probabilistic
computation. Specific methods will be explored to illustrate these approaches.
Module
Content
Survival Analysis
The Bootstrap and other resampling methods
Simulation based inference
Markov Chain Monte Carlo Methods
Recommended
Reading List
Module Pre
Requisite
Module Co
Requisite
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Module Descriptor 2012/13
School of Computer Science and Statistics.
Assessment
Details
Assessment is by written examination. To pass the module, students
must achieve an overall mark of 40%.
Module
approval date
N/a
Approved By
N/a
Academic
Start Year
N/a
Academic Year
N/a
of Data
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