Elements of Probability

Subject MAST90057 (2015)

Note: This is an archived Handbook entry from 2015.

Credit Points: 12.5
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2015:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period 02-Mar-2015 to 31-May-2015
Assessment Period End 26-Jun-2015
Last date to Self-Enrol 13-Mar-2015
Census Date 31-Mar-2015
Last date to Withdraw without fail 08-May-2015

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 36 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 Commitment:

170 hours


The following, or equivalent.

Study Period Commencement:
Credit Points:
Semester 1, Semester 2
Corequisites: None
Recommended Background Knowledge: 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.

Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability


Prof Konstantin Borovkov


Email: borovkov@unimelb.edu.au

Subject Overview:

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.

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.


50 pages of written assignments due during the semester (20%); a 45-minute computer laboratory test held during the semester (10%); a 3-hour written examination in the examination period (70%).

Prescribed Texts:

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

Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
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.

Related Course(s): Doctor of Philosophy - Business and Economics
Master of Commerce (Finance)
Master of Philosophy - Engineering
Master of Science (Bioinformatics)
Ph.D.- Engineering

Download PDF version.