This assessment is classified as Coursework as defined in the UCL Student Regulations for Exams and Assessments.
This ICA is worth 40 percent of your grade for this module.
The release date is 6 December 2024.
This ICA is due UCL Week 28, 7 March 2025, 13:00 London Time.
Individual extensions to the submission deadline can only be granted where a student has been issued with a Summary of Reasonable Adjustments (SoRA), has used a Delayed Assessment Permit (if the assessment is eligible), or has made a valid claim for Extenuating Circumstances. The standard extension length for this assessment type is 10 working days.
You must only submit your work via the designated portal in Moodle. If you try to submit via email or any other channel this will not count as a submission and will not be marked.
There are strict, non-negotiable penalties for late submission, which for coursework are as follows.
If the module lead becomes aware of a significant technical issue or outage affecting Moodle during the assessment, a message will be circulated to explain what has happened and the steps being taken to mitigate the issue. If you do not receive notification of a more widespread issue and you experience technical difficulties, you should refer to the Help & Support resources provided by UCL’s central IT service. However, last-minute technical issues will not be considered as valid grounds for missing the deadline, so ensure that you leave plenty of time to prepare, upload and check your submission.
Non-submission (in the absence of any valid Extenuating Circumstances) will mean that your mark for this component is recorded as 0.00% and you will be deemed to have made an attempt.
You should expect to receive feedback on this assessment within 20 working days of the submission deadline. In the event of a delay, the module lead will contact students directly with details of the revised timeline.
© (2024) UCL. This assessment paper is the intellectual property of UCL and subject to copyright. It must not be reproduced or shared with any third party without prior permission of UCL.
This ICA is to be completed in a group of 3-4; each member of the group will receive the same grade.
Your group-mates must be disjoint from that of ICA3, so that if Sam is in your ICA3 group, they can not be in your ICA2 group.
To facilitate anonymous marking, you should not write your name anywhere on your work, including in file names or file descriptions requested as part of the submission process.
A grading scheme is available here.
The ICA (written report) must be completed in R/Quarto/Python and typeset using Markdown with Latex code, just like the way our module content is generated.
Please hand in the html (or pdf file) and the Rmd/Jupyter notebook source files for your written report.
As usual, part of the grading will depend on the clarity and presentation of your project.
You are to do this assignment, within your groups, without any help from others who are not in your ICA2 group.
You are allowed to use any materials and code that was presented so far.
Inline with our usual class policy and how I have presented module material, do not use any fancy code or packages imported from elsewhere. In particular, if the main work/point of your project is the demonstrate branching processes, do not import or use a branching process package.
The use of ChatGPT and related tools in assisting in the project ICA2 is permitted, in accordance with UCL’s policy–Category 2.
Please read UCL guidelines on citation
In preparation for this assessment, please ensure that you are familiar with the Department of Statistical Science’s guidance on academic integrity. When submitting your work, you will be required to make a declaration that you have read and understood this guidance.
As this is a group project, collusion and academic misconduct cases will apply to all members in the group. All group members bear full responsibility. If a student suspects any potential misconduct cases from other groupmates, they should inform me immediately.
Please familarize yourself with the following guidance on academic integrity
In addition please note that parts of your submission may be scanned using similarity detection software. If any breach of the assessment regulations is suspected, it will be investigated in accordance with UCL’s Student Academic Misconduct Procedure.
By ticking the submission declaration box in Moodle you are agreeing to the following declaration:
Declaration: I am aware of the UCL Statistical Science Department’s regulations on plagiarism for assessed coursework. I have read the guidelines in the student handbook and understand what constitutes plagiarism. I hereby affirm that the work we are submitting for this in-course assessment is entirely our own.
Please do not write your names anywhere on the submission. Please include only your student numbers as a proxy identifier.
The following are some suggestions, but you are free to choose your own projects and directions. If you choose a project that is not listed here, I would like to have a chat with you and your group first to make sure it is suitable. Moreover, I am happy to chat with you about your project if you have any questions. It is possible that more than one group chooses the same or similar project. It is important that each group works on their own; in particular, two groups working on the same or similar project should not be helping each other on the project. Please feel free to discuss with me for more details and directions regarding the following suggestions.
There is a text (from a prison) that was coded using a Ceasar cipher; this was decoded using just pairwise probabilities and Markov chains. One project I had in mind was to decode this text or encode/decode other suitably available texts, and also see if using a longer memory makes a difference. background
Various projects on card shuffling. One would explore various shuffles and discuss using theory and simulation. For example, see these papers and the references within: overhand shuffle Shuffling cards and stopping times (use UCL VPN for link) How many times do I have to shuffle this deck
Queuing: central versus individual queues: does it make sense that banks use centralized queues and supermarkets often use individual queues? In fact, some supermarkets have a combination of individual and centralized queues; is there some optimal arrangement?
Queuing: block queuing: how many elevators do I need or should have at Heathrow airport? When should lifts that go to dedicated levels be used?
How to manage line-ups? Hospitals and other institutions put priority based on need, but you can’t make someone with a broken ankle wait forever?
How much staff do I need, at a hospital, if I want to ensure certain wait time benchmarks for patients.
How should planes be boarded and de-boarded? Develop models and test them. Compare with real world data.
Various models in statistical physics: eg: Discuss the Ising model, sample it using MCMC. For example, this paper considers exact sampling.
Applications of MCMC to Bayesian statistics: discuss the basic Bayesian framework, and how MCMC is applied, supporting the examples with code.
Discuss Quasi-Monte Carlo methods and illustrate theory with examples and simulations. There is an introduction here
Hidden Markov chains: Often the observations we see, do not correspond to a Markov chain, but in the background a Markov chain is pulling the strings.
How can the theory we learned be applied and extended to model epidemics? If we understand a model and the model is good, does it allow us to make interventions that help us improve the situation.
Write a report on a research paper, working through some of the theory and illustrating relevant examples via coding/simulation.
Write a report on a more advanced topic that was not covered in the module, and illustrate the relevant example via coding/simulation: eg solutions of PDE via probability theory, random walk and electrical networks.
Version: 06 December 2024