Probability Models 3 unit (KMA305)

Hobart

Introduction

The unit equips students to proficiently analyse real-world systems with inherent uncertainties using contemporary probability models. Skills and knowledge gained will be applicable to a broad spectrum of scientific and industrial challenges. It deepens understanding through both theory and practice, focusing on core concepts and real-world applications. The unit develops essential skills in applied probability theory, covering random variables, distributions, generating functions, and stochastic and Markovian processes, with an emphasis on intuition and physical interpretation. It provides tools to construct probability models for real-world problems and analyse their behaviour using modern approaches designed to meet the challenges of our evolving society. This unit is a core unit within the Statistics and Decision Science major (Bachelor of Science).

Summary

Unit name	Probability Models 3
Unit code	KMA305
Credit points	12.5
College/School	Sciences and Engineering School of Natural Sciences
Discipline	Mathematics
Coordinator	Associate Professor Malgorzata O'Reilly
Available as an elective?	Yes
Delivered By	University of Tasmania
Level	Advanced

Availability

Location	Study period	Attendance options		Available to
Hobart	Semester 1

Note

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Key Dates

Study Period	Start date	Census date	WW date	End date
Semester 1	22/2/2026	16/3/2026	19/4/2026	13/6/2026

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2026 are indicative and subject to change. Finalised census dates for 2026 will be available from the 1st October 2025. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

• Construct probability models for a range of real-world situations.
• Apply analytical techniques from probability theory and models to analyse abstract and real-world problems.
• Use mathematical language and notation to communicate probability theory and stochastic models to peers.
• State and use formal definitions and properties of structures within axiomatic theory of probability in a rigorous manner.

Fee Information

Field of Education	Commencing Student Contribution 1,3	Grandfathered Student Contribution 1,3	Approved Pathway Course Student Contribution 2,3	Domestic Full Fee 4
010101	$592.00	$592.00	not applicable	$2,919.00

• Available as a Commonwealth Supported Place
• HECS-HELP is available on this unit, depending on your eligibility³
• FEE-HELP is available on this unit, depending on your eligibility⁴

¹ Please refer to more information on student contribution amounts.
² Please refer to more information on eligibility and Approved Pathway courses.
³ Please refer to more information on eligibility for HECS-HELP.
⁴ Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Requisites

Prerequisites

Any intermediate level (200 coded) KMA unit

Teaching

Teaching Pattern	3 x 1-hr lectures, 1 x 1-hr tutorial , 1 x 1-hr lab weekly
Assessment	Demonstration (10%)|Examination (40%)|Assignment (multiple) (50%)
Timetable	View the lecture timetable | View the full unit timetable

Textbooks

Required	Required readings will be listed in the unit outline prior to the start of classes.
Recommended	• S. M. Ross, Introduction to Probability Models. • G. Grimmett and D. Stirzaker, Probability and Random Processes. • S. K. Karlin, A First Course in Stochastic Processes. • W. Feller, An Introduction to Probability Theory and Its Applications, vol. I. • Qi-Ming He, Fundamentals of matrix-analytic methods.

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.