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MATH3841 is a Mathematics Level III course.

Units of credit:Ìý6

Prerequisites: MATH3811 or MATH3911

Exclusions: MATH3820, MATH3870, MATH3920, MATH3941, MATH3970

Cycle of offering: Not offered each year.

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: The course outline contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its .

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

°Õ³ó±ðÌý contains up-to-date timetabling information.

If you are currently enrolled in MATH3841, you can log into  for this course.

The aim of this subject is to extend the student's understanding of statistical modelling, predominantly based upon independently distributed data, where dependence is required in the models. 

Measurements on different aspects of individual subjects are usually not independent. To successfully describe the relationships between the various measurements, models for correlation or dependence are required. Similarly, the successive observations on a time series (For example, those that occur in financial application) will exhibit serial dependence. Models which describe the serial dependence are useful for forecasting future values of the series. Spatially organised data, such as occur in environmental processes for example, similarly exhibit dependence between values observed at different sites.

The first half of the subject covers the multivariate normal distribution and the marginal and conditional distributions derived from it.  It also looks at various important properties concerning optimal prediction. The multivariate normal distribution is central to the practicising statistician's understanding of dependence between measurements within subjects, across time or space.

The second half of the subject builds on the basic properties of the multivariate normal distribution. We do this by applying the results to a series of examples drawn from time series and spatial processes.

Students who complete this course can expect to have obtained a good understanding of the importance of modelling dependence in observed data. You will alao gain an understanding of the basic distributions and models useful in a range of practical situations.