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Home PageELEC446.HomePage HistoryHide minor edits - Show changes to markup Changed line 53 from:
Module 412 in 2015, BUC5 compute resources, , Blockkurs on Markov Chain Monte Carlo for Inverse Problems in PDEs to:
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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. 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, and uses functions putgood.m and putbad.m. Changed line 47 from:
*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. to:
Changed line 47 from:
*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. 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, and uses functions putgood.m and putbad.m. Changed line 34 from:
*Paper on MCMC using an approximation, ADAMH to:
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*Paper on MTO for image deblurring to:
*Attach:Paper Δ on MTO for image deblurring Changed line 41 from:
* Useful code snippets: tauvsd.m (an example of running AM on multivariate normals), stdnorm_example.m (an example of using IA2RMS to sample from N(0,1)). to:
* Useful code snippets: tauvsd.m (an example of running AM on multivariate normals), stdnorm_example.m (an example of using IA2RMS to sample from N(0,1)), mcgaus.m (RWM for N(0,1)). Changed line 41 from:
* Useful code snippets: tauvsd.m (an example of running AM on multivariate normals), stdnorm_example.m (an example of using IA 2 RMS? to sample from N(0,1)). to:
* Useful code snippets: tauvsd.m (an example of running AM on multivariate normals), stdnorm_example.m (an example of using IA2RMS to sample from N(0,1)). Added line 41:
* Useful code snippets: tauvsd.m (an example of running AM on multivariate normals), stdnorm_example.m (an example of using IA 2 RMS? to sample from N(0,1)). Changed line 22 from:
* Inverse Problems 2016 version of the course notes to:
* ELEC 445 Inverse Problems course notes
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*John Bardsley's RTO paper to:
*Paper on MCMC using an approximation, ADAMH Changed lines 29-33 from:
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*AM: Roberts and Rosenthal 2009 paper Δ and Marko Laine's MCMC toolbox to:
*AM: Roberts and Rosenthal 2009 paper and Marko Laine's MCMC toolbox Changed line 27 from:
*AM: Roberts and Rosenthal 2009 paper and Marko Laine's MCMC toolbox to:
*AM: Roberts and Rosenthal 2009 paper Δ and Marko Laine's MCMC toolbox Changed line 24 from:
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*IA2RMS: paper and Matlab package to:
*IA2RMS: Martino Read Luengo 2015 paper and Matlab package Changed line 25 from:
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*IA2RMS: paper and Matlab package Changed line 26 from:
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Lectures and Tutorials: There is a 2-hour lecture and 1-hour tutorial per week Changed lines 24-27 from:
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*Ulli Wollf's UWerr MatLab code and documentation to:
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* Inverse Problems 2016 version of the course notes Δ to:
* Inverse Problems 2016 version of the course notes Changed line 20 from:
* Inverse Problems course notes notes on Statistical Inference to:
* Inverse Problems 2016 version of the course notes Δ Changed lines 2-3 from:
Module 412: Computational InferenceModule 412 develops Computational Inference: to:
ELEC 446: Computational InferenceELEC 446 develops Computational Inference: Changed lines 13-14 from:
Assessment: 30% assignments, 70% Exam to:
Assessment: 30% assignments, 70% Project (ELEC 446 will be internally assessed in 2016) Added line 20:
* Inverse Problems course notes notes on Statistical Inference Added lines 1-48:
(:notitle:) Module 412: Computational InferenceModule 412 develops Computational Inference: Bayesian inference for uncertainty quantification, stochastic modelling and MCMC sampling, advanced Monte Carlo strategies, imaging and machine vision. 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 Resources:
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