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
Select to view your study options…
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 element | Comments | % | ILO* |
---|---|---|---|
2.5 hour Exam | 60 | 01, 02, 03, 04, 05 | |
5 assignments (equiv 1500 words) | 30 | 04, 01, 03, 05, 02 | |
Essay (equiv 1500 words) | 10 | 04, 05, 06 |