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Probability & Inference in Biostatistics (MAST90100)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
About this subject
Contact information
Semester 1
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: https://study.unimelb.edu.au/
Overview
| Availability | Semester 1 - Dual-Delivery |
|---|---|
| Fees | Look up fees |
This subject covers the fundamental theory of probability and statistical inference that is needed as a foundation for understanding and practice of the core methods of biostatistics, understood as the science of drawing conclusions from data in health and medical investigations. Major topics include fundamental concepts of probability and distributions, including simulation of hypothetical data, and key concepts of statistical estimation and hypothesis testing, including sampling variability, confidence intervals, likelihood functions and an introduction to the Bayesian approach to inference. The approach emphasizes a critical understanding of the role of statistical inference in health research.
Intended learning outcomes
On completion of this subject, students should be able to:
- Demonstrate an understanding of the fundamental concepts of probability, including discrete and continuous probability distributions
- Apply calculus-based techniques to derive key features of a probability distribution and properties of random variables, such as mean and variance
- Recognise common probability distributions and their properties
- Demonstrate the role of simulation of random variables in understanding and explaining random variation and key ideas of statistical inference
- Explain and employ the key elements of statistical inference: target parameters, sampling variability, and frequentist methods including confidence intervals and hypothesis testing
- Explain the role of the likelihood function in parametric statistical inference, and be able to derive and interpret likelihood-based estimates for standard models
- Explain Bayesian concepts of post-data uncertainty and derive Bayesian inferences for simple standard models
- Critically interpret the commonly used tools of statistical inference, such as p-values, confidence intervals and Bayesian posterior distributions, as they are used in medical and scientific investigations
Generic skills
- Independent problem solving,
- Facility with abstract reasoning,
- Clarity of written expression,
- Sound communication of technical concepts
Last updated: 22 November 2024