Unit details

STAT4063 Computationally Intensive Methods in Statistics

Credit

6 points

Offering
(see Timetable)

Availability	Location	Mode
Semester 2	UWA (Perth)	Face to face

Details for undergraduate courses

• Level 4 elective
• Honours option in Mathematics and Statistics [Bachelor of Science (Honours)]

Content

The explosion in power of computers over recent times is changing the face of statistical science. Once upon a time, intuitively attractive statistical procedures had to be consigned to the waste bin if they led to unfathomable mathematical complications, or required masses of intricate calculations for their practical implementation. Now such difficulties can often be circumvented by using computer simulation and number crunching. This has led to the development and widespread use of many new statistical tools including the bootstrap, Markov Chain Monte Carlo methods and non-parametric kernel smoothing methods. Computer intensive statistical methods are not only in general use by statisticians, but are also applied by quantitative researchers in the life sciences, medicine and biological science, social sciences and business.

This unit gives a broad coverage of computer intensive methods with numerous applied examples, together with the underlying general concepts and basic theory. Particular emphasis is placed on the use of these methods in real statistical applications. Data sets are analysed using the statistical package R. Topics are selected from the following: simulation and Monte Carlo, bootstrap methods, Markov Chain Monte Carlo (MCMC) methods and Bayesian inference, non-parametric kernel smoothing methods, and statistical/machine learning.

Outcomes

Students are able to (1) appropriately apply computationally intensive statistical techniques in simulation studies and to real-world problems; (2) extend their knowledge of computational techniques in general, but statistical computing techniques in particular, and adapt known solutions to different situations; and (3) present results in a logical and coherent fashion and communicate effectively with others.

Assessment

Indicative assessments in this unit are as follows: (1) assignments and (2) a final examination. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Except where supplementary assessment is not available in a unit, it will be offered to students in all units who:

• Are in good academic standing overall;
• Have passed over half the units taken in the teaching period concerned, except where they are only enrolled in two or less units in the period;
• Have submitted all assessment items in the unit;
• Have achieved a mark between 45 and 49 for the unit overall, or the same mark in any failed component item in the unit; and
• No finding of academic misconduct has been made against them in the unit concerned.

Additionally student may apply for supplementary assessment in any unit which is the final unit required for graduation in there course and where they have achieved a mark between 45 and 49 for the unit overall, or the same mark in any failed component item in the unit.

Unit Coordinator(s)

Associate Professor Adriano Polpo de Campos

Unit rules

Prerequisites:

( Course Enrolment in the HON-MTHST Mathematics and Statistics [Honours] or the 62530 Master of Data Science ) and STAT3062 Statistical Science or STAT4064 Applied Predictive Modelling

Co-requisites:

Nil

Incompatibility:

Nil

Contact hours

3-hours per week