# Untitled

**Source**: https://teaching.scss.tcd.ie/wp-json/wp/v2/module/368
**Parent**: https://teaching.scss.tcd.ie/module/stu34505-modern-statistical-methods-i/

{"id":368,"date":"2023-07-31T14:18:30","date\_gmt":"2023-07-31T13:18:30","guid":{"rendered":"https:\/\/teaching.scss.tcd.ie\/?post\_type=module&p=368"},"modified":"2025-06-09T11:15:27","modified\_gmt":"2025-06-09T10:15:27","slug":"stu34505-modern-statistical-methods-i","status":"publish","type":"module","link":"https:\/\/teaching.scss.tcd.ie\/module\/stu34505-modern-statistical-methods-i\/","title":{"rendered":"STU34507 – Statistical Inference I"},"content":{"rendered":"\n

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| **Module Code<\/strong><\/td> STU34507<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Name<\/strong><\/td> Statistical Inference I<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **ECTS Weighting[**[1]<\/strong><\/a><\/strong><\/td> 5 ECTS<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Semester taught<\/strong><\/td> Semester 1<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Coordinator\/s  <\/strong><\/td> Prof. Simon Wilson<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nModule Learning Outcomes<\/h2>\n\n\n\n On successful completion of this module, students will be able to: <\/p>\n\n\n\n  \n1. Explain what subjective probability is and how Bayesian statistical inference is the result of adopting the subjective approach to probability can be motivated; <\/li>\n<\/ol>\n\n\n\n    \n1. Explain how Bayesian statistical inference is the result of adopting the subjective approach to probability;<\/li>\n<\/ol>\n\n\n\n       \n1. Contrast the Bayesian and frequentist approaches to statistical inference, explaining the meaning of a likelihood, parameter and probability model; <\/li>\n<\/ol>\n\n\n\n          \n1. Apply Bayes\u2019 Law to a given model and prior distribution to form a posterior distribution, and recognise the functional form of the common probability distributions; <\/li>\n<\/ol>\n\n\n\n             \n1. Summarise the different numerical analysis approaches to calculating the integrals involved in multi-dimensional posterior distributions or the calculation of marginal distributions from them; <\/li>\n<\/ol>\n\n\n\n                \n1. Describe the Monte Carlo approaches of rejection or importance sampling to approximate a given posterior distribution and estimate the normalising constant of a posterior distribution;<\/li>\n<\/ol>\n\n\n\n                   \n1. Demonstrate methods of elicitation of prior distributions. <\/li>\n<\/ol>\n\n\n\nModule Content<\/h2>\n\n\n\n This module will describe the theoretical and practical aspects of Bayesian statistics inference. <\/p>\n\n\n\n Specific topics addressed in this module include: Quantifying Uncertainty, Some Laws of Probability, Probability Models and Prior Distributions, Statistical Inference, Simple Examples: Conjugate Priors, A More Complex Example, Point and Interval Estimates, Numerical Methods of Computing Posterior Distributions, Basic Simulation Methods, Markov chain simulation, Prior Elicitation, Some Real Applications. <\/p>\n\n\n\nTeaching and learning Methods<\/h2>\n\n\n\n Lectures and tutorials. Lectures include some programming demonstrations.<\/p>\n\n\n\nAssessment Details<\/h2>\n\n\n\n                       |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |                      | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |                      | **Assessment Component<\/strong><\/td> **Brief Description<\/strong><\/td> **Learning Outcomes Addressed<\/strong><\/td> **% of total<\/strong><\/td> **Week set<\/strong><\/td> Week Due<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Examination<\/td> In person<\/td> All<\/td> 100<\/td> <\/td> \ufeff<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Group project<\/td> –<\/td> –<\/td> 0<\/td> –<\/td> \ufeff<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nReassessment Details<\/h2>\n\n\n\n \u00a0Examination (In person, 100%)<\/p>\n\n\n\nContact Hours and Indicative Student Workload<\/h2>\n\n\n\n  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Contact Hours (scheduled hours per student over full module), broken down by<\/strong>:<\/td> **33 hours<\/strong><\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Lectures<\/td> 27<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Tutorial or seminar<\/td> 6<\/td><\/tr>|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | **Independent study (outside scheduled contact hours), broken down by:<\/strong><\/td> **67 hours<\/strong><\/td><\/tr>|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | Preparation for classes and review of material (including preparation for examination, if applicable<\/td> 62<\/td><\/tr>|  |  |  |  | | --- | --- | --- | --- | | completion of assessments (including examination, if applicable)<\/td> 5<\/td><\/tr>|  |  | | --- | --- | | **Total Hours<\/strong><\/td> **hours<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nRecommended Reading List<\/h2>\n\n\n\n Lee, P.M., “Bayesian Statistics: an Introduction”, 2nd edition, published by Edward Arnold, 1997. <\/p>\n\n\n\n de Finetti, B., “Theory of Probability (Volumes 1 and 2), published by Wiley, 1990. <\/p>\n\n\n\n Lindley, D.V., ” Making Decisions”, 2nd edition, published by Wiley, 1985. <\/p>\n\n\n\n Ross, S.M. , ” Simulation”, 2nd edition, published by Academic Press, 1997. <\/p>\n\n\n\nModule Pre-requisites<\/h2>\n\n\n\n **Prerequisite modules:<\/strong> STU12501, STU12502, STU23501, STU22005<\/p>\n\n\n\n **Other\/alternative non-module prerequisites:<\/strong><\/p>\n\n\n\nModule Co-requisites<\/h2>\n\n\n\n None<\/p>\n\n\n\nModule Website<\/h2>\n\n\n\n [Blackboard<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" (Semester 1, 5 credits) This module introduces different statistical modelling used for analysing stochastic processes defined in the spatial and\/or time domains. 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