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Course Module Information
Course Modules
MA5116: Introductory Probability for Genomics
Semester 1 | Credits: 5
This module is designed to provide students with a grounding in probability theory, sufficient to enable them to learn about the application of probability models to genomics data. The module will apply probability to commonly encountered problems in genomics and provide students with experience of solving biological and biology-inspired problems involving probability.
(Language of instruction: English)
Learning Outcomes
- Demonstrate an understanding of concepts of probability, including set-theoretic description of probability
- Derive rules of probability from probability axioms
- Demonstrate an understanding of conditional probability, independence and Bayes' rule
- Use rules of probability for problem-solving including solving non-trivial problems in genomics
- Explain the concept of a random variable and describe common statistical models in terms of functions of random variables
- Use R to obtain maximum likelihood values for the parameters of probability models
- Select appropriate methods to compare the fit of alternative probability models
- Apply profile likelihood to obtain confidence intervals for maximum likelihood parameter estimates
Assessments
- Department-based Assessment (100%)
Teachers
- COLLETTE MCLOUGHLIN:
Research Profile |
Email
- CATHAL SEOIGHE:
Research Profile |
Email
- Pilib O Broin:
Research Profile |
Email
Note: Module offerings and details may be subject to change.