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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.
- 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: 3 November 2022