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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Probability II
Module Code MS232
School School of Mathematical Sciences
Module Co-ordinatorSemester 1: Martin Venker
Semester 2: Martin Venker
Autumn: Martin Venker
Module TeachersSonia Balagopalan
Martin Venker
NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

This module aims to introduce the students to the main techniques used when dealing with several random variables. It also offers an introduction to the limit theory of sequences of random variables. Students will attend lectures on the course material and will work, independently, to solve problems on topics related to the course material. The students will have an opportunity to review their solutions, with guidance, at weekly tutorials.

Learning Outcomes

1. express probabilities in terms of multible integrals and be able to evaluate such integrals
2. state selected definitions and theorems
3. solve problems that require the use of either one, several or infinitely many random variables
4. demonstrate an understanding of concepts by use of examples or counterexamples
5. explain arguments used to prove selected theorems

Workload Full-time hours per semester
Type Hours Description
Lecture24Students will attend lectures where new material will be presented and explained. Also attention will be drawn to various supporting material and tutorials as the course progresses.
Tutorial12Students will show their solutions to homework questions and will receive help with and feed-back on these solutions.
Independent Study89Corresponding to each lecture students will devote approximately one and a half additional hours of independent study to the material discussed in that lecture or to work on support material when attention is drawn to such in lectures. Before each tutorial students will devote approximately three and a half hours to solving homework problems which are to be discussed in that tutorial.
Total Workload: 125

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

Indicative Content and Learning Activities

Joint distribution, marginal distributions, conditioning on a random variable, transformation of random variables, independent random variables, convolution, characteristic function.

Equivalent characterisations of jointly normal random variables using mean vector and covariance matrix, densities, characteristic function or linear marginals. Linear transformations and conditioning.

Modes of convergence: almost sure convergence, convergence in L^p, in probability, in distribution. Relations between different modes of convergence. Tools for proving convergence: Chebyshev's and Markov's inequalities, Borel-Cantelli lemma, Levy's continuity theorem. Limit theorems: Weak and strong laws of large numbers, central limit theorem, Berry-Esseen's theorem.

Assessment Breakdown
Continuous Assessment20% Examination Weight80%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentStudents will submit solutions to exercises.20%As required
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 3
Indicative Reading List

  • Grimmett, G.R. and Stirzaker, D.R.:: 1992, Probability and Random Processes,, Oxford University Press,
  • Ross, S.: 2002, A First Course in Probability, Prentice Hall,
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