Modern Regression and Bayesian Methods

Content

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Description

Module Overview:  Modern Regression and Bayesian Methods

  • Random effects models
  • Modern regression (generalised additive models, etc)
  • Bayesian methods
  • Markov chain Monte Carlo methods
  • Case studies
Assessment

This module is assessed in two assignments. Each is a short project after each block of five lectures.  Deadlines will be confirmed in due course.

Prerequisites

Basic concepts in

  • probability (elementary probability distributions)
  • statistics (ideas of estimation, confidence intervals, hypothesis tests)
  • calculus
  • linear algebra

In addition, we assume the first semester module 'Regression and Simulation', or its equivalent.