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020 _a9780128016787
_qelectronic bk.
020 _a0128016787
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020 _z9780128013700
020 _a0128013702
020 _a9780128013700
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035 _a(OCoLC)906699032
_z(OCoLC)908100768
035 _a(OCoLC)ocn906699032
039 9 _y12-21-2016
_zhafiz
_wmetacoll.MYUKM.updates.D20160920.T210208.sdallbooks.1 (perolehan)hafizupload21122016.mrc
_x134
040 _aNST
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049 _aMAIN
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082 0 4 _a577.01/5195
_223
100 1 _aKorner-Nievergelt, Franzi,
_eauthor.
245 1 0 _aBayesian data analysis in ecology using linear models with R, BUGS, and Stan /
_cFr衮zi Korner-Nievergelt [and five others].
264 1 _aAmsterdam ;
_aBoston :
_bAcademic Press, an imprint of Elsevier,
_c[2015]
300 _a1 online resource :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aFront Cover; Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan; Copyright; Contents; Digital Assets; Acknowledgments; Chapter 1 -- Why do we Need Statistical Models and What is this Book About?; 1.1 WHY WE NEED STATISTICAL MODELS; 1.2 WHAT THIS BOOK IS ABOUT; FURTHER READING; Chapter 2 -- Prerequisites and Vocabulary; 2.1 SOFTWARE; 2.2 IMPORTANT STATISTICAL TERMS AND HOW TO HANDLE THEM IN R; FURTHER READING; Chapter 3 -- The Bayesian and the Frequentist Ways of Analyzing Data; 3.1 SHORT HISTORICAL OVERVIEW; 3.2 THE BAYESIAN WAY; 3.3 THE FREQUENTIST WAY
505 8 _a3.4 COMPARISON OF THE BAYESIAN AND THE FREQUENTIST WAYSFURTHER READING; Chapter 4 -- Normal Linear Models; 4.1 LINEAR REGRESSION; 4.2 REGRESSION VARIANTS: ANOVA, ANCOVA, AND MULTIPLE REGRESSION; FURTHER READING; Chapter 5 -- Likelihood; 5.1 THEORY; 5.2 THE MAXIMUM LIKELIHOOD METHOD; 5.3 THE LOG POINTWISE PREDICTIVE DENSITY; FURTHER READING; Chapter 6 -- Assessing Model Assumptions: Residual Analysis; 6.1 MODEL ASSUMPTIONS; 6.2 INDEPENDENT AND IDENTICALLY DISTRIBUTED; 6.3 THE QQ PLOT; 6.4 TEMPORAL AUTOCORRELATION; 6.5 SPATIAL AUTOCORRELATION; 6.6 HETEROSCEDASTICITY; FURTHER READING
505 8 _aChapter 7 -- Linear Mixed Effects Models7.1 BACKGROUND; 7.2 FITTING A LINEAR MIXED MODEL IN R; 7.3 RESTRICTED MAXIMUM LIKELIHOOD ESTIMATION; 7.4 ASSESSING MODEL ASSUMPTIONS; 7.5 DRAWING CONCLUSIONS; 7.6 FREQUENTIST RESULTS; 7.7 RANDOM INTERCEPT AND RANDOM SLOPE; 7.8 NESTED AND CROSSED RANDOM EFFECTS; 7.9 MODEL SELECTION IN MIXED MODELS; FURTHER READING; Chapter 8 -- Generalized Linear Models; 8.1 BACKGROUND; 8.2 BINOMIAL MODEL; 8.3 FITTING A BINARY LOGISTIC REGRESSION IN R; 8.4 POISSON MODEL; FURTHER READING; Chapter 9 -- Generalized Linear Mixed Models; 9.1 BINOMIAL MIXED MODEL
505 8 _a9.2 POISSON MIXED MODELFURTHER READING; Chapter 10 -- Posterior Predictive Model Checking and Proportion of Explained Variance; 10.1 POSTERIOR PREDICTIVE MODEL CHECKING; 10.2 MEASURES OF EXPLAINED VARIANCE; FURTHER READING; Chapter 11 -- Model Selection and Multimodel Inference; 11.1 WHEN AND WHY WE SELECT MODELS AND WHY THIS IS DIFFICULT; 11.2 METHODS FOR MODEL SELECTION AND MODEL COMPARISONS; 11.3 MULTIMODEL INFERENCE; 11.4 WHICH METHOD TO CHOOSE AND WHICH STRATEGY TO FOLLOW; FURTHER READING; Chapter 12 -- Markov Chain Monte Carlo Simulation; 12.1 BACKGROUND; 12.2 MCMC USING BUGS
505 8 _a12.3 MCMC USING STAN12.4 SIM, BUGS, AND STAN; FURTHER READING; Chapter 13 -- Modeling Spatial Data Using GLMM; 13.1 BACKGROUND; 13.2 MODELING ASSUMPTIONS; 13.3 EXPLICIT MODELING OF SPATIAL AUTOCORRELATION; FURTHER READING; Chapter 14 -- Advanced Ecological Models; 14.1 HIERARCHICAL MULTINOMIAL MODEL TO ANALYZE HABITAT SELECTION USING BUGS; 14.2 ZERO-INFLATED POISSON MIXED MODEL FOR ANALYZING BREEDING SUCCESS USING STAN; 14.3 OCCUPANCY MODEL TO MEASURE SPECIES DISTRIBUTION USING STAN; 14.4 TERRITORY OCCUPANCY MODEL TO ESTIMATE SURVIVAL USING BUGS
520 _aBayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions-including all R codes-that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.
588 0 _aOnline resource; title from PDF title page (Ebsco, viewed April 9, 2015).
590 _aElsevier
_bScienceDirect All Books
630 0 0 _aBUGS (Information storage and retrieval system)
650 0 _aEcology
_xResearch
_xStatistical methods.
650 0 _aBayesian statistical decision theory.
650 0 _aR (Computer program language)
650 7 _aNATURE / Ecology
_2bisacsh
650 7 _aNATURE / Ecosystems & Habitats / Wilderness
_2bisacsh
650 7 _aSCIENCE / Environmental Science
_2bisacsh
650 7 _aSCIENCE / Life Sciences / Ecology
_2bisacsh
650 7 _a菫ologie.
_0(DE-588)4043207-5
_2gnd
650 7 _aDatenverarbeitung.
_0(DE-588)4011152-0
_2gnd
650 7 _aBayes-Verfahren.
_0(DE-588)4204326-8
_2gnd
650 7 _aBiostatistik.
_0(DE-588)4729990-3
_2gnd
650 7 _aR.
_0(DE-588)4705956-4
_2gnd
650 7 _aGibbs-sampling.
_0(DE-588)4352359-6
_2gnd
650 4 _aBayesian statistical decision theory.
650 4 _aEcology -- Research -- Statistical methods.
650 4 _aEcology -- Study and teaching.
655 4 _aElectronic books.
655 0 _aElectronic books.
776 0 8 _iPrint version:
_tBayesian data analysis in ecology using linear models with R, BUGS, and Stan.
_dAmsterdam, [Netherlands] : Academic Press, c2015
_hxii, 316 pages
_z9780128013700
_w2014957273
856 4 0 _uhttp://ezplib.ukm.my/login?url=http://www.sciencedirect.com/science/book/9780128013700
907 _a.b16400525
_b2021-06-25
_c2019-11-12
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998 _ae
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