91果冻视频

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Bayesian Modelling

Audience: Academic staff, PG research, Professional staff, Research staff

Date: Tuesday 21 January 2025 to Thursday 23 January 2025

Times: 10.30 to 16.00

Key details: Bayesian modelling is a free course for University of 91果冻视频 staff and research degree students (see eligibility advice below). The course covers applied Bayesian inference to some common statistical inference problems such as linear and non-linear re

Target audience

The course is for University of 91果冻视频 students matriculated on a doctoral degree or the following research degree programmes 鈥� MSc (Res), MSt (Res), and MPhil (by research). The course is also open to University of 91果冻视频 staff. The course is not open to students on an undergraduate degree or to students on a taught postgraduate degree programme (MSc, MLitt, or MRes).

Qualifications needed

Entry Requirements and Prior Learning: * Prior exposure to probability theory, calculus and linear algebra * Some prior experience of frequentist statistics such as hypothesis testing and simple linear regression. The Introduction to Statistics course would be essential. * Some prior programming experience.

Course information

Content and Structure

The course covers three main parts:

  1. The modelling principles that underlie the Bayesian statistical paradigm
    1. The modelling principles of Bayesian inference
    2. Directed graphical models as a modelling tool
  2. Approximate Bayesian inference algorithms
    1. MCMC algorithms
  3. Applied Bayesian modelling with probabilistic programming languages
    1. Regression problem
    2. Classification problem
    3. Generalised regression problem

The methods covered will be implemented using a new programming language, Julia, and the course includes a substantial practical component. 听

The course lasts 2.5 days; the morning sessions will cover theory and there will be computer practicals and exercises in the afternoons.听

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Aims and objectives

Learning Outcomes

After taking the course, participants should be able to:

  • Understand the principles that underline the Bayesian statistical paradigm
  • Understand the needs of the Bayesian statistical paradigm in machine learning and statistical learning
  • Understand the main computational algorithms for implementing Bayesian statistical inference
  • Use probabilistic programming languages to do applied Bayesian modelling and computation

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Timetable and Location

The course will take place on the following dates and will be held in-person:

  • Dates: Tuesday 21 January to Thursday 23 January 2025. 听Participants need to be available for all days.
  • Time: 10:30am 鈥� 4pm on Tuesday and Wednesday. 10:30am 鈥� 1pm on Thursday.
  • Location: Maths Institute Lecture Theatre B with computer practicals taking place in the Maths Institute PC classroom. 听

What is provided?

Course materials - lecture slides, notes, and exercises - will be available online.

Refreshments are not provided but there are cafes close by in the School of Physics and the Medical Sciences Building where refreshments can be purchased.

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Venue

CREEM

Course provider

St Leonard's Postgraduate CollegeEmail: stlc@st-andrews.ac.uk