Probability for Statistics (MAST20006)

Undergraduate level 2Points: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
Semester 2
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This subject develops the probability theory that is necessary to understand statistical inference. Properties of probability are reviewed, random variables are introduced, and their properties are developed and illustrated through common univariate probability models. Models for the joint behavior of random variables are introduced, along with conditional probability and Markov chains. Methods for obtaining the distributions of functions of random variables are considered along with techniques to obtain the exact and approximate distributions of sums of random variables. These methods will be illustrated through some well known normal approximations to discrete distributions and by obtaining the exact and approximate distributions of some commonly used statistics. Computer packages are used for numerical calculations but no programming skills are required.

Intended learning outcomes

At the completion of the subject, students are expected to:



  • Develop a systematic understanding of probability, random variables, probability distributions and probability models, and their relevance to statistical inference;
  • Be able to formulate standard probability models from real world applications and critically assess them;
  • Be able to apply the properties of probability distributions, moment generating functions, variable transformations and conditional expectations to analyse common random variables and probability models;
  • Be able to use a computer package to perform computational tasks in probability analyses.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they should progressively acquire generic skills from this subject that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time management skills: the ability to meet regular deadlines while balancing competing commitments; and
  • becoming familiar with statistical computing packages.

Last updated: 9 April 2025

Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
MAST10006 Calculus 2 No longer available
MAST10009 Accelerated Mathematics 2 No longer available
MAST10021 Calculus 2: Advanced No longer available

MAST10019 Calculus Extension Studies

One of

Code Name Teaching period Credit Points
MAST10007 Linear Algebra No longer available
MAST10008 Accelerated Mathematics 1 No longer available
MAST10010 Data Analysis No longer available
MAST10011 Experimental Design and Data Analysis No longer available
MAST10022 Linear Algebra: Advanced No longer available

MAST10018 Linear Algebra Extension Studies

MAST10013 UMEP Maths for High Achieving Students

OR

Admission into or selection of one of the following:

  • 150pt Program course entry point in the MC-SCIBIF Master of Science (Bioinformatics)
  • 200pt Program course entry point in the MC-SCIBIF Master of Science (Bioinformatics)

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
MAST20004 Probability No longer available

MAST30015 Statistics for Mechanical Engineers (prior to 2011)

ELEN30002 Stochastic Signals and Systems (prior to 2011)

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 9 April 2025

Assessment

DescriptionTimingPercentage

Five written assignments due at regular intervals amounting to a total of up to 20 hours (4 hours each)

  • 240 minutes (each)
During the teaching period20%

A computer laboratory test

  • 45 minutes
End of semester10%

A written examination

  • 3 hours
During the examination period70%

Last updated: 9 April 2025

Dates & times

  • Semester 1
    CoordinatorYao-Ban Chan
    Mode of deliveryOn Campus (Parkville)
    Contact hours3 x one hour lectures per week, 1 x one hour practice class per week, and 1 x one hour computer laboratory class per week
    Total time commitment170 hours
    Teaching period 3 March 2025 to 1 June 2025
    Last self-enrol date14 March 2025
    Census date31 March 2025
    Last date to withdraw without fail 9 May 2025
    Assessment period ends27 June 2025

    Semester 1 contact information

    yaoban@unimelb.edu.au

  • Semester 2
    CoordinatorKonstantin Borovkov
    Mode of deliveryOn Campus (Parkville)
    Contact hours3 x one hour lectures per week, 1 x one hour practice class per week, and 1 x one hour computer laboratory class per week
    Total time commitment170 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    borovkov@unimelb.edu.au

Time commitment details

Estimated total time commitment of 170 hours

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 9 April 2025

Further information

Last updated: 9 April 2025