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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Stochastic Modelling
Module Code MS308
School School of Mathematical Sciences
Module Co-ordinatorSemester 1: Martin Venker
Semester 2: Martin Venker
Autumn: Martin Venker
Module TeachersMartin Venker
NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat examination

To give a comprehensive introduction to Markov chains, Markov jump processes and their application to actuarial science.

Learning Outcomes

1. Construct Markov chain models for actuarial and financial processes.
2. Analyse any given chain in a systematic way, including determining its asymptotic behaviour.
3. Prove the main theorems governing Markov chains in discrete and continuous time.
4. State the definitions of the main concepts underlying the theory of Markov chains and demonstrate an understanding of these through examples and counter-examples.

Workload Full-time hours per semester
Type Hours Description
Lecture36No Description
Tutorial12No Description
Independent Study150No Description
Total Workload: 198

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

Stochastic Modelling
Review of basic probabilistic concepts, the various types of stochastic processes, stationarity, Markov processes, the Chapman-Kolmogorov equations, stationary probability distributions. [CS2 - 3.1]

Markov Chains
Solution of the Chapman-Kolmogorov equation in matrix form, transition graph, finding the stationary distribution, actuarial examples; two-state chains; the limiting distribution of finite Markov chains, irreducibility and aperiodicity, exponential convergence; infinite Markov chains, criteria for recurrence, the limiting distribution and its relation to mean recurrence times; applications: queues, random walks with various boundary conditions. [CS2 - 3.2]

Markov Jump Processes
The infinitesimal generator, the forward and backward equations, solution in exponential form; holding times, exponential distribution, jump chain; the limiting distribution of a finite Markov jump process and its connection to mean recurrence times; the case of infinite state spaces, the integral form of the backward equation, the minimal process, conservative processes; the Poisson process and actuarial models; inhomogeneous Markov jump processes, time-dependent transition rates, the backward equation in differential and integral forms, residual holding times. [CS2 - 3.3].

Assessment Breakdown
Continuous Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
In Class Testn/a25%Week 9
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

  • Bhattacharya, R.N., and Waymire R.C: 1990, Stochastic Processes with Applications, Wiley, NewYork,
  • Grimmett, G.R. and Stirzaker, D.R.: 1992, Probability and Random Processes, 2-nd, Oxford UP, Oxford,
  • Norris, JR: 1997, Markov Chains, Cambridge UP, Cambridge,
  • A.N. Other: 0, Acted material for CT4 subject , models ,
Other Resources

Programme or List of Programmes
ACMBSc in Actuarial Mathematics
Date of Last Revision03-JUL-09

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