PROBABILITY MODELS

STM4PM

Not currently offered

Credit points: 15

Subject outline

The analysis of scientific, engineering and economic data makes extensive use of probability models. This subject describes the most basic of these models and their properties. Specific topics covered in this subject include a wide range of discrete and continuous univariate distributions; joint distributions; conditional expectation; mean and variance of linear combinations of random variables; Chebyshev's inequality; moment generating functions; the law of large numbers; the Central Limit Theorem.  The relevance of probability models to data science is explored through supplementary reading and a project.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorMarcel Jackson

Available to Study Abroad StudentsYes

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites Admission into the Master of Data Science (SMDS)

Co-requisitesN/A

Incompatible subjects STA2MD AND STA2MDA AND STM2PM

Equivalent subjectsN/A

Special conditionsN/A

Graduate capabilities & intended learning outcomes

01. Model and solve problems when randomness is involved

Activities:
Modelled in lectures, and practised in tutorials

02. Compute/derive mathematical calculations to investigate numerical properties of probability models

Activities:
Modelled in lectures, and practised in tutorials

03. Derive some basic probability results in selected areas of application

Activities:
Modelled in lectures, and practised in tutorials

04. Defend or question the validity of different probability models

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Literacies and Communication Skills

05. Present clear, well structured explanations of numerical results including the appropriate use of statistical and mathematical vocabulary

Activities:
Modelled in lectures, and practised in tutorials and on formative assignments, with particular emphasis on the work students complete at home with time to proof-read
Related graduate capabilities and elements:
Literacies and Communication Skills
Inquiry and Analytical Skills
Discipline -Specific Knowledge and Skills

06. Explain the relevance of probability models to data science in an area of interest.

Activities:
Reading of supplementary reading and independently to present a short essay on probability models as relevant to area such as genomics or public health data
Related graduate capabilities and elements:
Literacies and Communication Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Discipline -Specific Knowledge and Skills

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