000			07100cam a2200757Ii 4500
001			ocn906699032
003			OCoLC
005			20190328114810.0
006			m o d
007			cr cnu|||unuuu
008			150407s2015 ne a ob 001 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dN$T _dIDEBK _dOPELS _dE7B _dYDXCP _dCOO _dCDX _dCHVBK _dEBLCP _dDEBSZ _dFEM _dOCLCO _dIDB _dZ5A _dOCLCQ _dMERUC _dOCLCQ _dWRM _dU3W _dD6H _dOCLCF _dRRP _dOCLCQ _dWYU _dOCLCA _dMERER _dOCLCQ
019			_a908100768 _a926105501 _a968096815 _a1066538951
020			_a9780128016787 _q(electronic bk.)
020			_a0128016787 _q(electronic bk.)
020			_a0128013702
020			_a9780128013700
020			_z9780128013700
035			_a(OCoLC)906699032 _z(OCoLC)908100768 _z(OCoLC)926105501 _z(OCoLC)968096815 _z(OCoLC)1066538951
050		4	_aQH541.15.S72
070	0		_aQH541.15.S72 _bK67 2015
072		7	_aNAT _x010000 _2bisacsh
072		7	_aNAT _x045040 _2bisacsh
072		7	_aSCI _x026000 _2bisacsh
072		7	_aSCI _x020000 _2bisacsh
082	0	4	_a577.01/5195 _223
100	1		_aKorner-Nievergelt, Fr�anzi, _eauthor.
245	1	0	_aBayesian data analysis in ecology using linear models with R, BUGS, and Stan / _h[electronic resource] _cFr�anzi 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
347			_atext file _2rda
588	0		_aOnline resource; title from PDF title page (Ebsco, viewed April 9, 2015).
504			_aIncludes bibliographical references and index.
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.
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.
630	0	0	_aBUGS (Information storage and retrieval system)
630	0	7	_aBUGS (Information storage and retrieval system) _2fast _0(OCoLC)fst01897696
650		0	_aEcology _xResearch _xStatistical methods.
650		0	_aBayesian statistical decision theory.
650		0	_aR (Computer program language)
650		7	_aNATURE _xEcology. _2bisacsh
650		7	_aNATURE _xEcosystems & Habitats _xWilderness. _2bisacsh
650		7	_aSCIENCE _xEnvironmental Science. _2bisacsh
650		7	_aSCIENCE _xLife Sciences _xEcology. _2bisacsh
650		7	_aBayesian statistical decision theory. _2fast _0(OCoLC)fst00829019
650		7	_aR (Computer program language) _2fast _0(OCoLC)fst01086207
650		7	_a�Okologie _2gnd _0(DE-588)4043207-5
650		7	_aDatenverarbeitung _2gnd _0(DE-588)4011152-0
650		7	_aBayes-Verfahren _2gnd _0(DE-588)4204326-8
650		7	_aBiostatistik _2gnd _0(DE-588)4729990-3
650		7	_aR _gProgramm _2gnd _0(DE-588)4705956-4
650		7	_aGibbs-sampling _2gnd _0(DE-588)4352359-6
650		2	_aBayes Theorem.
650		2	_aLinear Models.
655		4	_aElectronic books.
655		0	_aElectronic book.
776	0	8	_iPrint version: _tBayesian data analysis in ecology using linear models with R, BUGS, and Stan. _dAmsterdam, [Netherlands] : Academic Press, �2015 _hxii, 316 pages _z9780128013700
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128013700
999			_c247071 _d247071