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Elements of Probability (MAST90057)

Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)

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Overview

Availability
Semester 1 - Dual-Delivery
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Randomness is inherent in biological data and the analysis of data arising in both Bioinformatics and Biostatistics requires knowledge of sophisticated probability models and statistical techniques. This subject develops the underlying probability theory that is necessary to understand these models and techniques. Computer packages are used for numerical and theoretical calculations but no programming skills are required. Elements of Probability will be co-taught with MAST20006 Probability for Statistics.

Intended learning outcomes

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




 

  • have developed 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 biological applications and critically assess them;
  • be able to apply the properties of probability distributions, to analyse common random variables and probability models; and
  • be able to use a computer package to perform algebraic and computational tasks in probability analyses.

Generic skills

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;
  • become familiar with a major statistical computing package.

Last updated: 26 February 2023

Eligibility and requirements

Prerequisites

Code Name Teaching period Credit Points
MAST10005 Calculus 1
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
 12.5

OR

Equivalent subject

OR

Admission into or selection of one of the following:

  • MC-SCIBIF Master of Science (Bioinformatics)
  • MC-COMPBIO Master of Computational Biology

Corequisites

None

Non-allowed subjects

Students who have previously taken second year level subjects in Probability or Probability for Statistics or their equivalents may not gain credit for this subject.

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: 26 February 2023

Assessment

DescriptionTimingPercentage

Written assignments

  • 50 pages
During the teaching period20%

A computer laboratory test

  • 45 minutes
During the teaching period10%

A written examination

  • 3 hours
During the examination period70%

Last updated: 26 February 2023

Dates & times

  • Semester 1
    Principal coordinatorSophie Hautphenne
    Mode of deliveryDual-Delivery (Parkville)
    Contact hours36 hours: Three x 1-hour lectures per week, one x 1-hour practice classes per week, and one x 1-hour computer laboratory classes per week.
    Total time commitment170 hours
    Teaching period28 February 2022 to 29 May 2022
    Last self-enrol date11 March 2022
    Census date31 March 2022
    Last date to withdraw without fail 6 May 2022
    Assessment period ends24 June 2022

    Semester 1 contact information

    sophiemh@unimelb.edu.au

Time commitment details

170 hours

Last updated: 26 February 2023

Further information

  • Texts

    Prescribed texts

    Hogg and Tanis, Probability and Statistical Inference. Eighth Edition, Prentice Hall, 2009.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Commerce (Finance)
    CourseMaster of Philosophy - Engineering
    CoursePh.D.- Engineering
    CourseMaster of Science (Bioinformatics)
    CourseDoctor of Philosophy - Engineering
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 26 February 2023