stm4pm probability models

PROBABILITY MODELS

STM4PM

2017

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(Writing,Quantitative Literacy)

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(Writing,Quantitative Literacy)
Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
Discipline -Specific Knowledge and 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(Writing,Quantitative Literacy)
Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)

Subject options

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Start date between: and    Key dates

Melbourne, 2017, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorMarcel Jackson

Class requirements

LectureWeek: 31 - 43
Three 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

PracticalWeek: 31 - 43
One 1.0 hours practical per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments%ILO*
2.5 hour Exam6001, 02, 03, 04, 05
5 assignments (equiv 1500 words)3004, 01, 03, 05, 02
Essay (equiv 1500 words)1004, 05, 06