From Physics @ Otago

ELEC446: BUC 5

Resources for BUC 5 computing labs:

Instructors: Colin Fox and J Andres Christen

• Some Reading
  • lecture notes (so far)
  • Gareth Roberts' notes on Statistical Inference has useful statements of basic MCMC methods and theorems
• Compute 1 (for m-files, right click to 'Save As' to get formatting correct)
  • mcmc.m
  • mcgauss.m
  • Ulli Wollf's UWerr MatLab code and documentation
  • Code to plot IACT as function of window using mcgauss.m
  • Code to sample inverse heat problem
  • heatfem.m that builds FEM matrices for heat problem
• Python code
  • Andres' zip file
• Compute 2
  • Complete your sampler for the inverse problem in (constant) D
  • Tune the window size for your RWM sampler, or better still (challenge question) plot a graph of IACT as function of window w to find the optimal w
  • Consider error in the final time T, T~Unif[1.8,2.2]. Perform joint inference for D and T, and say whether inference for D is significantly altered.
  • Evaluate the linearized forward map for D(x)->u(x,T), using the secant method and the simple FEM program supplied. What is the effective rank of this map?
• Some good books on FEM
  • Hackbusch
  • Ern and Guermond
• Lecture 3
  • slides
• Compute 3
  • Task sheet
  • Image of good and bad cells in Matlab mat format slide.mat, or as slide.tif. Matlab code makefake.m that generated the image, and uses functions putgood.m and putbad.m.
• Public Lecture
  • slides
  • movie Δ