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Bioinformatics involves the analysis of biological data and randomness is inherent in both the biological processes themselves and the sampling mechanisms by which they are observed. This subject first introduces stochastic processes and their applications in Bioinformatics, including evolutionary models. It then considers the application of classical statistical methods including estimation, hypothesis testing, model selection, multiple comparisons, and multivariate statistical techniques in Bioinformatics.
Intended learning outcomes
At the conclusion of this subject, students should be able to:
- understand some of the common stochastic models encountered in Bioinformatics;
- apply a variety of statistical techniques to problems arising in Bioinformatics.
- Problem-solving skills including engaging with unfamiliar problems and identifying relevant 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 an analysis; Through interaction
Last updated: 2 December 2019