Courses & Units

Probability Models 3 KMA305




The unit develops foundation skills for the analysis of real-life systems with elements of uncertainty, useful for careers in the Physical and Biological Sciences, Operations Research, Statistics, Engineering, Computer Science, Finance and Economics. The unit covers major topics from Probability Theory, with the focus on developing in-depth knowledge from both theoretical and modelling points of view.
Topics: Axiomatic probability theory: sample space, event, probabilities on events, independent events, Bayes' formula; Random variable, probability distribution, expectation, conditional probability; Distribution functions: discrete, continuous; joint distribution; probability generating function; Laplace transform; moment generating function; limit theorems. Stochastic Processes: Bernoulli process; Poisson process; discrete-time Markov Chains: Chapman-Kolmogorov equations, classification of states, recurrence, limiting probabilities; continuous-time Markov Chains: Kolmogorov differential equations, embedded chains, equilibrium distributions.
Students will use MATLAB for the numerical experimentation.


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


Location Study period Attendance options Available to
Hobart Semester 1 On-Campus International Domestic


International students
Domestic students

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Key Dates

Study Period Start date Census date WW date End date
Semester 1 22/2/2021 23/3/2021 12/4/2021 30/5/2021

* 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 2021 are indicative and subject to change. Finalised census dates for 2021 will be available from the 1st October 2020. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

  • Prove and apply a range of standard concepts and results from the probability theory, and give their physical interpretations.
  • Observe the patterns in the MATLAB output and interpret them using the physical interpretations, detailing what you observe in the output, what it tells you about the process, and why it makes sense in terms of the dynamics of the underlying process.
  • Understand the concept of probability and its properties, and apply the axioms and basic rules, including Chain Rule, Law of Total Probability and Bayes’s rule.
  • Apply appropriate discrete/continuous time distributions to solve problems. Give their physical interpretations and derive their properties.
  • Calculate probability distribution function given weights of a discrete random variable, and vice versa.
  • Calculate probability distribution function given density function of a continuous random variable, and vice versa.
  • Calculate probability distribution function of a mixed random variable.
  • Apply joint distribution and conditional distribution in solving problems.
  • Evaluate and apply probability generating function/Laplace-Stieltjes transform/moment generating function of a given distribution.
  • Apply standard limit theorems.
  • Define Bernoulli process and give the physical interpretations of corresponding discrete time random variables in terms of its performance measures. Apply it in solving problems.
  • Define Poisson process (in two different ways) and give the physical interpretations of corresponding discrete time and continuous time random variables in terms of its performance measures. Apply it in solving problems.
  • Define discrete-time Markov Chains (DTMC) and continuous-time Markov Chain (CTMC), and give the physical interpretations of their parameters and performance measures.
  • Model and solve a given problem as a DTMC or a CTMC.
  • Determine communicating classes, transient/recurrent states of a given DTMC.
  • Calculate probability distribution at time n/mean recurrence times/stationary distribution for a given DTMC.
  • Construct the corresponding embedded chain from a given CTMC.
  • Calculate the steady state distribution for a given CTMC.
  • Establish transience or recurrence of states of a CTMC using the embedded chain.
  • Apply Kolmogorov Differential Equations for a CTMC.
Field of Education Commencing Student Contribution 1 Grandfathered Student Contribution 1 Approved Pathway Course Student Contribution 2 Domestic Full Fee
010101 $493.00 $493.00 not applicable $2,402.00
  • 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.

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Any Intermediate unit in Mathematics


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