LEADER 00993nam2-2200313---450- 001 990009901860403321 005 20141006104008.0 035 $a000990186 035 $aFED01000990186 035 $a(Aleph)000990186FED01 035 $a000990186 100 $a20141006d1969----km-y0itay50------ba 101 0 $aeng 102 $aGB 105 $aa-------101yy 200 1 $a<<2.: >>Properties of rocks$edams and underground construction$epapers 5-10 210 $aLondon$cBritish Geotechnical Society$dc1969 215 $aP. 37-86$cill.$d30 cm 300 $aIn testa al front.: British Geotechnical Society 461 0$1001000990179$12001$aConference on in situ investigations in soils and rocks$v2 610 0 $aMeccanica delle rocce$aConvegni 712 02$aBritish Geotechnical Society 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990009901860403321 952 $aZA 10/25$bs.i.$fDINGE 959 $aDINGE 996 $aProperties of rocks$9828595 997 $aUNINA LEADER 01168nam0-2200301 --450 001 9910318830503321 005 20190521120142.0 010 $a9780953983858 100 $a20190520d2010----kmuy0itay5050 ba 101 0 $amul 102 $aGB 105 $aa 101yy 200 1 $aTeaching writing, learning to write$eproceedings of the XVIth Colloquium of the Comité International de Paléographie Latine, held at the Institute of English Studies, University of London, 2-5 September 2008$fedited by P. 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Stauffer 210 $aHoboken, N.J. $cWiley-Interscience$dc2008 215 $a1 online resource (418 p.) 300 $aDescription based upon print version of record. 311 $a0-470-16504-9 320 $aIncludes bibliographical references (p. 383-387) and index. 327 $aCONTEMPORARY BAYESIAN AND FREQUENTIST STATISTICAL RESEARCH METHODS FOR NATURAL RESOURCE SCIENTISTS; CONTENTS; Preface; 1 Introduction; 1.1 Introduction; 1.2 Three Case Studies; 1.2.1 Case Study 1: Maintenance of a Population Parameter above a Critical Threshold Level; 1.2.2 Case Study 2: Estimation of the Abundance of a Discrete Population; 1.2.3 Case Study 3: Habitat Selection Modeling of a Wildlife Population; 1.2.4 Case Studies Summary; 1.3 Overview of Some Solution Strategies; 1.3.1 Sample Surveys and Parameter Estimation; 1.3.2 Experiments and Hypothesis Testing 327 $a1.3.3 Multiple Linear Regression, Generalized Linear Modeling, and Model Selection1.3.4 A Preview of Bayesian Statistical Inference; 1.3.5 A Preview of Model Selection Strategies and Information-Theoretic Criteria for Model Selection; 1.3.6 A Preview of Mixed-Effects Modeling; 1.4 Review: Principles of Project Management; 1.5 Applications; 1.6 S-Plus® and R Orientation I: Introduction; 1.6.1 Orientation I; 1.6.2 Simple Manipulations; 1.6.3 Data Structures; 1.6.4 Random Numbers; 1.6.5 Graphs; 1.6.6 Importing and Exporting Files; 1.6.7 Saving and Restoring Objects; 1.6.8 Directory Structures 327 $a1.6.9 Functions and Control Structures1.6.10 Linear Regression Analysis in S-Plus and R; 1.7 S-Plus and R Orientation II: Distributions; 1.7.1 Uniform Distribution; 1.7.2 Normal Distribution; 1.7.3 Poisson Distribution; 1.7.4 Binomial Distributions; 1.7.5 Simple Random Sampling; 1.8 S-Plus and R Orientation III: Estimation of Mean and Proportion, Sampling Error, and Confidence Intervals; 1.8.1 Estimation of Mean; 1.8.2 Estimation of Proportion; 1.9 S-Plus and R Orientation IV: Linear Regression; 1.10 Summary; Problems; 2 Bayesian Statistical Analysis I: Introduction; 2.1 Introduction 327 $a2.1.1 Historical Background2.1.2 Limitations to the Use of Frequentist Statistical Inference for Natural Resource Applications: An Example; 2.2 Three Methods for Fitting Models to Datasets; 2.2.1 Least-Squares (LS) Fit-Minimizing a Goodness-of-Fit Profile; 2.2.2 Maximum-Likelihood (ML) Fit-Maximizing the Likelihood Profile; 2.2.3 Bayesian Fit-Bayesian Statistical Analysis and Inference; 2.2.4 Examples; 2.3 The Bayesian Paradigm for Statistical Inference: Bayes Theorem; 2.4 Conjugate Priors; 2.4.1 Continuous Data with the Normal Model; 2.4.2 Count Data with the Poisson Model 327 $a2.4.3 Binary Data with the Binomial Model2.4.4 Conjugate Priors for Other Datasets; 2.5 Other Priors; 2.5.1 Noninformative, Uniform, and Proper or Improper Priors; 2.5.2 Jeffreys Priors; 2.5.3 Reference Priors, Vague Priors, and Elicited Priors; 2.5.4 Empirical Bayes Methods; 2.5.5 Sensitivity Analysis: An Example; 2.6 Summary; Problems; 3 Bayesian Statistical Inference II: Bayesian Hypothesis Testing and Decision Theory; 3.1 Bayesian Hypothesis Testing: Bayes Factors; 3.1.1 Proportion Estimation of Nesting Northern Spotted Owl Pairs; 3.1.2 Medical Diagnostics; 3.2 Bayesian Decision Theory 327 $a3.3 Preview: More Advanced Methods of Bayesian Statiscal Analysis-Markov Chain Monte Carlo (MCMC) Alogrithms and WinBUGS Software 330 $aThe first all-inclusive introduction to modern statistical research methods in the natural resource sciences The use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixed-effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. 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