PROBABILITY MODELS

STM2PM

2018

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. To help students with little or no previous knowledge of calculus, the equivalent of one lecture per week consists of relevant mathematical topics to support the other content.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorMarcel Jackson

Available to Study Abroad StudentsYes

Subject year levelYear Level 2 - UG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites (MAT1CDE and MAT1NLA) or MAT1CLA or STA1SS or STA1LS or STA1PSY

Co-requisitesN/A

Incompatible subjects STA2MD AND STA2MDA

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
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills

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

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills

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

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills

04. Defend or question the validity of different probability models

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Discipline -Specific Knowledge and 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:
Discipline -Specific Knowledge and Skills

Subject options

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Start date between: and    Key dates

Melbourne, 2018, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorMarcel Jackson

Class requirements

Lecture
Three 1.0 hours lecture per week on weekdays during the day and delivered via face-to-face.

Tutorial
One 1.0 hours tutorial per week on weekdays during the day and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
2.5 hour Exam70 05, 03, 02, 04
5 assignments (1500 words equivalent total)30 04, 01, 03, 05, 02