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My thesis consisted of four published papers, one unpublished
chapter, and `summary' and `postscript' chapters.
If you are only interested in one published chapter then
you might prefer to download the single `Neural Computation' files listed after each chapter
below. The thesis version is to be preferred, however, as it
contains slightly more information. The whole thing, postscript | djvu | pdf
- Contents
- pp. i-vi
(30K); |DJVU|
- Chapter 1: Summary
-
pp. 01-06 (40K);
|DJVU|
- Chapter 2: Bayesian Interpolation
-
pp. 07-17 (86K);
|DJVU|
pp. 18-25 (68K);
|DJVU|
pp. 26-33 (91K);
|DJVU|
or
as published in Neural Computation
4 3 415-447 (188K).
|DJVU|
- Chapter 3: A Practical Bayesian Framework for Backprop Networks
-
pp. 34-43 (94K);
|DJVU|
pp. 44-52 (71K);
|DJVU|
or as published in Neural Computation
4 3 448-472 (144K).|
|DJVU|
- Chapter 4: Information-based objective
functions for active data selection
-
pp. 53-64 (122K); |DJVU|
or as published in Neural Computation
4 4 589-603 (125K). |DJVU|
- Chapter 5: The evidence framework applied to classification networks
-
pp. 65-69
(85K); |DJVU|
pp. 70-70
(195K); |DJVU|
pp. 71-78
(191K); |DJVU|
or as published in Neural Computation
4 5 698-714
(456K).
|DJVU|
- Chapter 6: Inferring input-dependent
noise levels
-
pp. 79-81
(34K); |DJVU|
- Chapter 7: Postscript
-
pp. 82-86
(39K);
|DJVU|
- Bibliography
-
pp. 87-92
(30K).
|DJVU|
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The Inference Group is supported by the Gatsby Foundation and by a partnership award from IBM Zurich Research Laboratory
David J.C. MacKaySite last modified Mon Aug 23 18:20:36 BST 2004
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