| Record ID | marc_columbia/Columbia-extract-20221130-007.mrc:443417239:3350 |
| Source | marc_columbia |
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LEADER: 03350fam a22003854a 4500
001 3434771
005 20221020073248.0
008 021112s2003 nyua b 001 0 eng
010 $a 2002042667
020 $a0387001360 (pbk. : acid-free paper)
035 $a(OCoLC)51022678
035 $a(OCoLC)ocm51022678
035 $9AVR6617CU
035 $a(NNC)3434771
035 $a3434771
040 $aDLC$cDLC$dNNC$dOrLoB-B
042 $apcc
050 00 $aG70.3$b.S57 2003
082 00 $a910/.01/5195$221
245 00 $aSpatial statistics and computational methods /$ceditor, Jesper Møller.
260 $aNew York :$bSpringer,$c2003.
263 $a0212
300 $axiv, 202 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aLecture notes in statistics ;$v173
504 $aIncludes bibliographical references and index.
505 00 $g1.$tAn Introduction to MCMC -- $g1.1.$tMCMC and spatial statistics -- $g1.2.$tThe Gibbs sampler -- $g1.3.$tThe Metropolis-Hastings algorithm -- $g1.4.$tMCMC Theory -- $g1.5.$tPractical implementation -- $g1.6.$tAn illustrative example -- $g1.7.$tAppendix: Model determination using MCMC -- $g2.$tAn Introduction to Model-Based Geostatistics -- $g2.1.$tIntroduction -- $g2.2.$tExamples of geostatistical problems -- $g2.3.$tThe general geostatistical model -- $g2.4.$tThe Gaussian Model -- $g2.5.$tParametric estimation of covariance structure -- $g2.6.$tPlug-in prediction -- $g2.7.$tBayesian inference for the linear Gaussian model -- $g2.8.$tA Case Study: the Swiss rainfall data -- $g2.9.$tGeneralised linear spatial models -- $g2.10.$tDiscussion -- $g2.11.$tSoftware -- $g2.12.$tFurther reading -- $g3.$tA Tutorial on Image Analysis -- $g3.1.$tIntroduction -- $g3.2.$tMarkov random field models -- $g3.3.$tModels for binary and categorical images -- $g3.4.$tImage estimators and the treatment of parameters --
505 80 $g3.5.$tGrey-level images -- $g3.6.$tHigh-level imaging -- $g3.7.$tAn example in ultrasound imaging -- $g4.$tAn Introduction to Simulation-Based Inference for Spatial Point Processes -- $g4.1.$tIntroduction -- $g4.2.$tIllustrating examples -- $g4.3.$tWhat is a spatial point process? -- $g4.4.$tPoisson point processes -- $g4.5.$tSummary statistics -- $g4.6.$tModels and simulation-based inference for aggregated point patterns -- $g4.7.$tModels and simulation-based inference for Markov point processes -- $g4.8.$tFurther reading and concluding remarks.
520 1 $a"The volume introduces topics of current interest in spatial and computational statistics, which should be accessible and of interest to postgraduate students as well as to experienced statistical researchers. It is partly based on the course material for the "TMR and MaPhySto Summer School on Spatial Statistics and Computational Methods," held at Aalborg University, Denmark, August 19 to 22, 2001."--BOOK JACKET.
650 0 $aGeography$xStatistical methods.$0http://id.loc.gov/authorities/subjects/sh85054000
650 0 $aSpatial analysis (Statistics)$0http://id.loc.gov/authorities/subjects/sh85126347
700 1 $aMøller, Jesper.$0http://id.loc.gov/authorities/names/n94006074
830 0 $aLecture notes in statistics (Springer-Verlag) ;$vv. 173.$0http://id.loc.gov/authorities/names/n42015168
852 00 $bmat$hG70.3$i.S57 2003