Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Repeat examination This module is in resit category 3: no resit of the continuous assessment is available. |
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Description MS117 aims to introduce the basic concepts of probability theory through a mixture of lectures and problem solving based tutorials. The module will give students a working knowledge of the main techniques of elementary probability and build a solid foundation for learning more advanced topics in probability and statistics. | |||||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Define elementary concepts of probability and state the main theorems. 2. Use summation, integration, counting techniques and approximations to assign probabilities to events or compute distribution functions. 3. Compute and apply conditional probabilities. 4. Derive the basic properties of common discrete, continuous and mixed distributions. 5. Compute expectation, median and variance of given distributions and prove their theoretical properties. | |||||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Principles of Modelling Chance Probability spaces, construction of probability measures via densities, distribution functions Conditional Probabilities and Independence Conditional probabilities, law of total probability, Bayes theorem, independence Standard Models In Probability Combinatorics, random variables, common distributions in urn models: multinomial, binomial, (multivariate) hypergeometric, discrete and continuous waiting time distributions Characteristics of Random Variables Expectation, median, variance, standard deviation Approximations of the Binomial Distribution Poisson approximation, normal approximation | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||