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tu |
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231223s1995 xx ||||| 00| ||eng c |
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|a pubmed25n0593.xml
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|a (DE-627)NLM177782404
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|a (NLM)18291974
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Lange, K
|e verfasserin
|4 aut
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|a Globally convergent algorithms for maximum a posteriori transmission tomography
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|c 1995
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 02.10.2012
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|a Date Revised 22.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a This paper reviews and compares three maximum likelihood algorithms for transmission tomography. One of these algorithms is the EM algorithm, one is based on a convexity argument devised by De Pierro (see IEEE Trans. Med. Imaging, vol.12, p.328-333, 1993) in the context of emission tomography, and one is an ad hoc gradient algorithm. The algorithms enjoy desirable local and global convergence properties and combine gracefully with Bayesian smoothing priors. Preliminary numerical testing of the algorithms on simulated data suggest that the convex algorithm and the ad hoc gradient algorithm are computationally superior to the EM algorithm. This superiority stems from the larger number of exponentiations required by the EM algorithm. The convex and gradient algorithms are well adapted to parallel computing
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|a Journal Article
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| 700 |
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|a Fessler, J A
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 4(1995), 10 vom: 15., Seite 1430-8
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
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|g volume:4
|g year:1995
|g number:10
|g day:15
|g pages:1430-8
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