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October 13, 2014, at 05:13 PM by 139.80.236.40 -
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  • Lecture 8: Counting objects
October 13, 2014, at 04:06 PM by 139.80.236.40 -
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 *Image of good and bad cells in Matlab mat format slide.mat, or as slide.tif. Matlab code makefake.m that generated the image.
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 *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.
October 13, 2014, at 03:31 PM by 139.80.236.40 -
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 *Image of good and bad cells in Mat Lab? mat format slide.mat, or as slide.tif. Matlab code makefake.m that generated the image.
to:
 *Image of good and bad cells in Matlab mat format slide.mat, or as slide.tif. Matlab code makefake.m that generated the image.
October 13, 2014, at 03:30 PM by 139.80.236.40 -
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 *Matlab code for deblurring jupiter image:  jupiter1.tif  jupiter.mdeconv1.m
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 *Matlab code for deblurring jupiter image:  jupiter1.tif  jupiter.m  deconv1.m
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 *Matlab code for deblurring jupiter image:  jupiter1.tif  deconv1.m
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 *Matlab code for deblurring jupiter image:  jupiter1.tif  jupiter.mdeconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif (right click, save as)  http://www.physics.otago.ac.nz/data/fox/BigFiles/abramowitz_and_stegun.tar.gz. jupiter1.tif deconv1.m
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 *Matlab code for deblurring jupiter image:  jupiter1.tif  deconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif (right click, save as) jupiter.m deconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif (right click, save as)  http://www.physics.otago.ac.nz/data/fox/BigFiles/abramowitz_and_stegun.tar.gz. jupiter1.tif deconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif jupiter.m deconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif (right click, save as) jupiter.m deconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif jupiter.m deconv1.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif jupiter.m deconv1.m
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 *John Bardsley's RTO paper 
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 *John Bardsley's RTO paper 
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 *John Bardsley's RTO paper 
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 *Matlab code for deblurring jupiter image: jupiter.tiff Δ heat.m
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 *Matlab code for deblurring jupiter image: jupiter1.tif jupiter.m deconv1.m
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  • Lecture 6: Image Reconstruction
  • Lecture 7: Linear/Gaussian problems
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August 19, 2014, at 05:12 PM by 139.80.236.40 -
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Lecturer: Colin Fox. Room 503. Ph 4797806. colin.fox 'at' otago.ac.nz

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Lecturer: Colin Fox. Room 503. Ph 4797806. colin.fox 'at' otago.ac.nz

August 19, 2014, at 05:11 PM by 139.80.236.40 -
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Module 405: Computational Inference

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Module 412: Computational Inference

August 19, 2014, at 05:09 PM by 139.80.236.40 -
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  1. Lecture 1: Problem and Goals
    1. Gareth Roberts' notes on Statistical Inference
  2. Lecture 2: Expectations
    1. Adaptive rejection paper by Gilks Tan Best
  3. Lecture 3: MCMC basics
  4. Lecture 3: Algorithmic Efficiency
    1. Ulli Wollf's UWerr MatLab code and documentation
  5. Assignment 1:
to:
  • Lecture 1: Problem and Goals
    • Gareth Roberts' notes on Statistical Inference
  • Lecture 2: Expectations
    • Adaptive rejection paper by Gilks Tan Best
  • Lecture 3: MCMC basics
  • Lecture 4: Algorithmic Efficiency
    • Ulli Wollf's UWerr MatLab code and documentation
  • Lecture 5: Inverse Diffusion Problem
  • Assignment 1:
August 19, 2014, at 05:04 PM by 139.80.236.40 -
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  *Gareth Roberts' notes on Statistical Inference
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  #Gareth Roberts' notes on Statistical Inference
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  *Adaptive rejection paper by Gilks Tan Best
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  #Adaptive rejection paper by Gilks Tan Best
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  *
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  #
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  *Ulli Wollf's UWerr MatLab code and documentation
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  #Ulli Wollf's UWerr MatLab code and documentation
August 19, 2014, at 05:03 PM by 139.80.236.40 -
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  *Matlab code for diffusion problem: Main.m [Attach:heat.m | heat.m]]
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  *Matlab code for diffusion problem: Main.m heat.m
August 19, 2014, at 05:02 PM by 139.80.236.40 -
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  1. Assignment 1: Problem sheet
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  1. Assignment 1:
August 19, 2014, at 05:01 PM by 139.80.236.40 -
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  1. Assignment 1: Attach:Ass1_2014.pdf
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  1. Assignment 1: Problem sheet
August 19, 2014, at 04:28 PM by 139.80.236.40 -
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  1. Assignment 1: Attach:Ass1_2014.pdf
August 19, 2014, at 08:13 AM by 121.98.152.24 -
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Links:

Department of Physics 400-level information

August 12, 2014, at 07:45 AM by 121.98.152.86 -
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  1. Lecture 1: Problem and Goals''
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  1. Lecture 1: Problem and Goals
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  1. Lecture 2: Expectations''
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  1. Lecture 2: Expectations
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  1. Lecture 3: MCMC basics''
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  1. Lecture 3: MCMC basics
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  1. Lecture 3: Algorithmic Efficiency''
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  1. Lecture 3: Algorithmic Efficiency
August 12, 2014, at 07:44 AM by 121.98.152.86 -
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Module 405: Computational Inference (ELEC)

Module 412 deals with Computational Inference:

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Module 405: Computational Inference

Module 412 develops Computational Inference:

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advanced Monte Carlo methods, imaging and machine vision.

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advanced Monte Carlo strategies, imaging and machine vision.

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Resources:

  1. Lecture 1: Problem and Goals''
    • Gareth Roberts' notes on Statistical Inference
  2. Lecture 2: Expectations''
    • Adaptive rejection paper by Gilks Tan Best
  3. Lecture 3: MCMC basics''
  4. Lecture 3: Algorithmic Efficiency''
    • Ulli Wollf's UWerr MatLab code and documentation
August 12, 2014, at 07:30 AM by 121.98.152.86 -
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  • Recommended Preparation: Inverse Problems and Imaging module
  • Semester: 2
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Semester: 2

Recommended Preparation: Inverse Problems and Imaging module

Lecturer: Colin Fox. Room 503. Ph 4797806. colin.fox 'at' otago.ac.nz

Assessment: 30% assignments, 70% Exam

July 16, 2014, at 03:50 PM by 139.80.236.40 -
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Module 405: Computational Inference

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Module 405: Computational Inference (ELEC)

July 16, 2014, at 03:47 PM by 139.80.236.40 -
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(:notitle:)

Module 405: Computational Inference

May 22, 2013, at 11:09 AM by 139.80.236.40 -
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Module 412 deals with Computational Inference: Bayesian inference for uncertainty quantification, stochastic modelling and MCMC sampling, advanced Monte Carlo methods, imaging and machine vision.

  • Recommended Preparation: Inverse Problems and Imaging module
  • Semester: 2