Statistical Analysis and Probability (993G5)
Statistical Analysis and Probability
Module 993G5
Module details for 2025/26.
15 credits
FHEQ Level 7 (Masters)
Module Outline
This module will allow you to develop numerous practical real-world examples will be discussed during practical sessions and analysed using the Python programming language.
Indicative Content
• Probability: Random variables and probability distributions, expectation and interpretation of moments, conditional probability and Bayes’ rule, conditional expectation and properties.
• Frequentist statistics: likelihood, point estimators, hypothesis testing, interval estimators (confidence intervals and their connection with hypothesis tests), Central Limits theory (consistency, asymptotic normality, chi square approximation).
• Bayesian Statistics: The Bayesian paradigm, Bayesian models and prior distributions.
• Model Selection: Frequentist model selection, Bayesian model selection and Bayes factors.
Module learning outcomes
Systematically understand the concepts and methods of statistical inference and be able to apply these methods in practical situations and as a part of a decision making process.
Display command of the following intellectual and practical skills: Write programs for Bayesian inference and model selection.
Critically analyse, interpret and appraise articles on Statistics.
Commence scientific and technical writing skills for continuing professional development.
| Type | Timing | Weighting |
|---|---|---|
| Computer Based Exam | Semester 1 Assessment | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
Dr Vladislav Vysotskiy
Assess convenor
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