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Index > F - Pg. 643

Index 643 Metropolis algorithm and, 134­136 of parameter values, 13, 106­107 Estimation (single-prior) approach, 296­297 credible value, 296 null value of difference, credible value and, 297­301 null value of parameter, credible value and, 297 parameter value and, 296 ROPE of, 301­303 Estimation/model comparison, 310­312 null value probability, true, 310­311 R Code for, 312­314 recommendations for, 311­312 ROPE combined with, 310­311 Evidence Bayes' rule and, 57 data value prediction and, 63 FilconBrugsPower.R, 323, 330, 332, 333, 341­346, 347, 409, 489 FilconBrugs.R, 222­223, 224f, 232­235, 236­237, 285f, 315 FilconCoKappaBrugs.R, 237 FilconModelCompBrugs.R, 246 FilconModelCompPseudoPriorBrugs.R, 250­251, 263 Filtration-condensation experiment, 227, 298­299, 307, 330, 557 categorization rules for, 220 hierarchical dependencies and, 221­222 hierarchical diagram for, 221f models used for, 223­224 pseudopriors and, 246 R code for, 231­235 Fisher, Ronald, 53 Folded-t prior, for noise/error, 494 Font size in R plot, 17, 545 Formal analytical solution, of model-comparison approach, two or more nominal variables predicting frequency, 381­383 genPriorMean, 336 genPriorN, 336 Gibbs sampling, 167­173, 171f, 425 benefits/limitations of, 156, 177 binomial proportions via, 155­190 conditional posterior probability distributions and, 170­171 disadvantages of, 172­173 equations for, 170 limitations of, 168 Metropolis algorithm v., 171­172 notations for, 157 parameters for, 168­169 prior/likelihood/posterior for, 157­159 procedure for, 168, 169f, 172 GLM. See Generalized linear model; generalized linear model Goal