MTH6102 - Bayesian Statistical Methods - 2023/24
Topic outline
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View all general news and announcements from the your module leaders.
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Forum Description: This forum is available for everyone to post messages to. Students can raise questions or discuss issues related to the module. Students are encouraged to post to this forum and it will be checked daily by the module leaders. Students should feel free to reply to other students if they are able to.
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Practical tutorial for IT class
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R code for practical 1
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Exercise for you to practise (not to be handed in)
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Updated slides: 9-10
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R code illustration
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Data set to be used in IT class for practical 2
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Deadline: 16th October, 11:00am
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Please upload your assignment, exercise sheet 2, here
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The lecture slides of class 11/10
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Updated with review - 11/10/2023
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Exercise sheet for practice (not to be handed in)
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Deadline: Monday, 30 October, at 11:00
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Data set to be used in exercise sheet problem 1
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Upload your assignment exercise sheet 4
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Exercise sheet for practice
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This practical focuses on posterior estimates and credible intervals
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Deadline: Monday the 13th November at 11am.
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Updated with review
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This example on multiple parameters will help you for problem 3 of exercise sheet 6
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Updated 17/11
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Deadline: Monday, 11th December, 11:00am
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R code for lecture 10A that implements MH on the log scale for the exponential/gamma example
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R code for lecture 10A that shows how to compute the acceptance probability for different values of the proposal standard deviation and how to use burn in.
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Exercise sheet on Bayes factors
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Last topic
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This does not cover everything on the final! Look at the exercise sheets for other topics
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solutions to selected problems
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Assessment Pattern
The assessment for this module will involve two components:
- Five in-term coursework assignments, worth 4% each;
- A final exam worth 80%.
The coursework, worth 20%, is made up of five sets of exercise sheet questions, to be submitted every two weeks. The first questions to be submitted will be posted at 11:00 on Monday of week 2 (2/10). There will be a link in the week's content on QMPlus to submit your solutions. The first deadline to submit solutions is at 11am on Monday of week 4 (16/10).
Format and dates for the in-term assessments -
Please, see the tentative schedule in the syllabus section in Important module information.You can submit a Word document, pdf or a clearly legible image of hand-written work. The answers can be brief - you only need to answer what is explicitly asked for; but please use some words to state what each part of the answer is. So if the question asks for a maximum likelihood estimate, then say e.g. "The MLE for sigma is ...", rather than just writing a number, or a symbol and number. For questions that use R, you need to submit the R code, and also write out the answers and submit those in a separate document.
Some of the assessed questions use datasets on QMPlus. There is a different dataset for each student: details will be given in the exercise sheet. You need to be logged in to QMPlus to see the dataset. If you cannot see a file, please send me an email.
Late submission: I will be posting solutions to the in-term coursework as soon as the deadline passes, so I won't be able to accept late submissions. If you are prevented from submitting by circumstances beyond your control, then you can submit an extenuating circumstances claim (talk to the Maths office about this). If this is accepted, then this coursework can be excluded from your final module mark.Format of final assessment -
The remaining 80% of the assessment is the final exam, which is a traditional written exam, not computer-based. This will take place in January 2024.
You are allowed to bring 3 pages of A4 notes as well as a non-programmable calculator.
Past exams
The past exam papers uploaded in Module Content will give you some idea of the style of the final exam.Description of Feedback -
Your feedback comes in many forms
- Exercise sheets every two weeks with feedback
- Weekly lab tutorials
- Extra exercise sheet to work through each week for practice
- Participation in the IT classes
- Personalised feedback during office hours of the organiser -
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Other points to note are:
- there is an appendix with common probability distributions;
- in that appendix are listed two percentiles of the standard normal distribution, below the entry for the normal distribution - the exam will not use statistical tables, as nothing else from the tables apart from these percentiles will be needed;
- calculators are permitted.
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Swirl is a package within in R that contains interactive lessons
There is a website r-bloggers.com, contributed to by various R users. It has a page of online R resources:
https://www.r-bloggers.com/2015/12/how-to-learn-r-2/
This includes some online video material, but most of this is paid-for. A couple of free options (at least for now) that it lists are:
https://www.udemy.com/course/introduction-to-r
https://www.coursera.org/learn/probability-intro?specialization=statistics
The Cross Validated question and answer website is a good place to look for help, by searching previous questions:
https://stats.stackexchange.com/
If you ask a question yourself, you need to be precise about what the problem is, and what you have tried already.
The online manual on the official R (Cran) website is organized like a book, although it goes into more details of technicalities than you might want:
https://cran.r-project.org/doc/manuals/R-intro.html
There is a lot of other free online written material about R:
https://cran.r-project.org/ -
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