Undergraduate Programme and Module Handbook 2019-2020 (archived)
Module MATH2667: MONTE CARLO II
Department: Mathematical Sciences
MATH2667:
MONTE CARLO II
| Type |
Open |
Level |
2 |
Credits |
10 |
Availability |
Available in 2019/20 |
Module Cap |
|
Location |
Durham
|
Prerequisites
- (Calculus and Probability 1 (MATH1061) and Linear
Algebra 1 (MATH1071) and Programming and Dynamics 1 (MATH1041))
OR (Calculus and Probability 1 (MATH1061) and Linear Algebra 1
(MATH1071) and Discovery Skills in Physics (PHYS1011)) OR
(Calculus and Probability 1 (MATH1061) and Linear Algebra 1
(MATH1071) and Computational Thinking (COMP1051))
Corequisites
Excluded Combination of Modules
Aims
- To provide a working knowledge of the theory, computation
and practice of Monte Carlo (stochastic) simulation and an
introduction to stochastic modelling.
Content
- Foundations of the Monte Carlo method
- Random number generation
- Generating random variables
- Stochastic modelling
- Advanced topics from: Markov chain Monte Carlo,
variance reduction, continuous time models
Learning Outcomes
- By the end of the module students will:
- be able to solve novel and/or complex random number
generation and distribution sampling problems,
- be able to build and/or extend simple stochastic
models,
- have acquired programming skills in python related to
stochastic modelling.
- In addition students will have specialised
mathematical skills in the following areas which can be used
with minimal guidance: Modelling, Computation.
- Synthesis of data, critical and analytical thinking,
computer skills
Modes of Teaching, Learning and Assessment and how these contribute to
the learning outcomes of the module
- Lectures demonstrate the theory to be learned and the
application of the theory to practical examples.
- Computer practical sessions develop and practice
programming and modelling skills, and provide active engagement
and feedback to the learning process.
- Tutorials develop theoretical knowledge and provide
active engagement and feedback to the learning
process.
- Fortnightly theoretical provide formative assessment
to guide students in the correct development of their knowledge
and skills.
- The computer-based practical examination assesses the
ability to use programming skills to solve predictable and
unpredictable problems.
- The end-of-year written examination assesses the
acquired knowledge from a more conceptual viewpoint, including
mastery of theoretical aspects underpinning practical
applications.
Teaching Methods and Learning Hours
| Activity |
Number |
Frequency |
Duration |
Total/Hours |
|
| Lectures |
22 |
2 per week in Epiphany and in first week of Easter |
1 Hour |
22 |
|
| Tutorials |
5 |
Fortnightly in Epiphany and one in Easter |
1 Hour |
5 |
■ |
| Computer Practicals |
10 |
Weekly in Epiphany |
1 Hour |
10 |
■ |
| Preparation and Reading |
|
|
|
63 |
|
| Total |
|
|
|
100 |
|
Summative Assessment
| Component: Computer Practical Examination |
Component Weighting: 25% |
| Element |
Length / duration |
Element Weighting |
Resit Opportunity |
| Practical examination |
2 hours |
100% |
Yes |
| Component: Written Examination |
Component Weighting: 75% |
| Element |
Length / duration |
Element Weighting |
Resit Opportunity |
| End of year written examination |
90 minutes |
100% |
Yes |
One written assignment to be handed in every
fortnight in Epiphany. Weekly quizzes in Computer Practical
Sessions.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University
