| Record ID | marc_columbia/Columbia-extract-20221130-009.mrc:145742280:1455 |
| Source | marc_columbia |
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LEADER: 01455pam a22002894a 4500
001 4108735
005 20221027035742.0
008 020709s2003 mdua b 001 0 eng
010 $a 2002073958
020 $a0801872200 (alk. paper)
035 $a(OCoLC)ocm50184931
035 $a(NNC)4108735
035 $a4108735
040 $aDLC$cDLC$dYDX$dOrLoB-B
042 $apcc
050 00 $aQC174.85.B38$bL45 2003
082 00 $a530.15/95$221
100 1 $aLemm, Jörg C.$0http://id.loc.gov/authorities/names/n2002158800
245 10 $aBayesian field theory /$cJörg C. Lemm.
260 $aBaltimore, Md. :$bJohns Hopkins University Press,$c2003.
300 $axix, 411 pages :$billustrations ;$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
504 $aIncludes bibliographical references (p. 365-402) and index.
505 00 $g1.$tIntroduction -- $g2.$tBayesian framework -- $g3.$tGaussian prior factors -- $g4.$tParameterizing likelihoods: Variational methods -- $g5.$tParameterizing priors: Hyperparameters -- $g6.$tMixtures of Gaussian prior factors -- $g7.$tBayesian inverse quantum theory (BIQT) -- $g8.$tSummary -- $gA.$tA priori information and a posteriori control -- $gB.$tProbability, free energy, energy, information, entropy, and temperature -- $gC.$tIteration procedures: learning.
650 0 $aBayesian field theory.$0http://id.loc.gov/authorities/subjects/sh2002004440
852 00 $bmat$hQC174.85.B38$iL45 2003