PROBABILITY MODELS

STM2PM

2020

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.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Nadiya Kosytsina

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 2 - UG

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: STA1SS OR MAT1CLA OR STA1PSY OR STA1LS OR (MAT1CDE AND MAT1NLA)

Co-requisites: N/A

Incompatible subjects: STA2MD

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

DISCIPLINE KNOWLEDGE AND SKILLS

Intended Learning Outcomes

01. Model and solve problems when randomness is involved
02. Compute/derive mathematical calculations to investigate numerical properties of probability models
03. Derive some basic probability results in selected areas of application
04. Defend or question the validity of different probability models
05. Present clear, well structured explanations of numerical results including the appropriate use of statistical and mathematical vocabulary

Melbourne (Bundoora), 2020, Semester 2, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Subject Instance Co-ordinator: Nadiya Kosytsina

Class requirements

LectureWeek: 0 - 0
Three 1.00 hour lecture per week on weekdays during the day from week 0 to week 0 and delivered via face-to-face.

TutorialWeek: 0 - 0
One 1.00 hour tutorial per week on weekdays during the day from week 0 to week 0 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

2 hour Exam (Equivalent to 3000 words)

N/AN/AN/ANo70SILO2, SILO3, SILO4, SILO5

5 assignments (1500 words equivalent total)

N/AN/AN/ANo30SILO1, SILO2, SILO3, SILO4, SILO5