Авторизация
Поиск по указателям
Gelman A., Carlin J.B., Stern H.S. — Bayesian data analysis
Обсудите книгу на научном форуме Нашли опечатку?
Название: Bayesian data analysisАвторы: Gelman A., Carlin J.B., Stern H.S.Аннотация: Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critiques statistical analysis from a Bayesian perspective. Changes in the new edition include: added material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, added chapter on further computation topics, more examples, and additional chapters on current models for Bayesian data analysis such as equation models, generalized linear mixed models, and more. The book is an introductory text and a reference for working scientists throughout their professional life.
Язык: Рубрика: Математика /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Издание: 2nd editionГод издания: 2004Количество страниц: 668Добавлена в каталог: 11.02.2006Операции: Положить на полку |
Скопировать ссылку для форума | Скопировать ID
Предметный указатель
Survey incentives, example of meta-analysis and decision analysis 544—552 Surveys 207—218 see Surveys, adolescent smoking 172—174 Surveys, Alcoholics Anonymous 216—218 Surveys, pre-election polling 210—214 428—430 526—539 Surveys, telephone, unequal sampling probabilities 242—244 t model see "Student-t model" Tail-area probabilities see "p-values" Tan, K. 309 Tan, M. 348 Tan, W. 151 Tanner, M. 308 309 331 539 Taplin, R. 458 Target distribution (for iterative simulation) 275 Tarone, R. 151 Taylor, J. 457 Tebaldi, C. 440 Teller, A. 308 Teller, E. 308 Terry, M. 440 Test statistics and test quantities 161 Test statistics and test quantities, choosing 172 Test statistics and test quantities, examples see "Model checking" Test statistics and test quantities, graphical 165- 172 Test statistics and test quantities, numerical 172—177 Teukolsky, S. 331 348 584 Teutsch, S. 569 Thacker, S. 569 Thayer, D. 411 494 Thinning of MCMC sequences 295 Thisted, R. 308 332 Thomas, A. 29 151 152 191 192 309 608 Thomas, C. 238 Thomas, N. 238 Thompson, E. 348 349 Thornton, J. 255 Three steps of Bayesian data analysis 3 259 Tiao, G. 28 29 66 94 151 191 238 385 457 480 493 Tibshirani, R. 191 255 257 Tierney, L. 191 308 348 Time series models 491—494 Time series models, frequency domain 492 Time series models, time domain 491 Titterington, D. 66 480 To reduce correlations in hierarchical models 509 Tolerance intervals 71 Toxicology model, as example of an ill-posed system 504—514 Trans-dimensional MCMC 338—339 348 Transformations 24 114 Transformations, logarithmic 387 Transformations, logistic (logit, log-odds) 24 146 Transformations, not needed for incentives example 547 Transformations, not needed for incumbency example 363 Transformations, power 195—196 265—269 Transformations, probit 25 Transformations, rat tumor example 128 Transformations, to improve MCMC efficiency 302—305 Transformations, useful in setting up a multivariate model 489 Treatment variable 353 Triest, R. 237 539 Tritchler, D. 151 Truncated data 231—236 Tsui, K. 190 Tsutakawa, R. 494 Tuerlinckx, F. 30 255 439 Tufte, E. 192 Tukey, J. 192 Tukey, P. 29 Turner, D. 480 Tversky, A. 28 568 Type I errors, why we do not care about 175 U.S. Census 261 428 539 U.S. House of Representatives 359 Ulam, S. 308 Unbiasedness see "Bias" Unbounded likelihoods 109 Underidentified models 108 Uniform distribution 573 574 UNITS 354 Unnormalized densities 8 275 Unseen species, estimating the number of 332 Utility in decision analysis 543 553 557 567 Vaida, F. 191 Vaidyanathan, S. 191 Vail, A. 255 Validation 159 253 Van Buuren, S. 539 van der Scheuren, E. 255 van Dyk, D. 309 331 412 440 457 Van Hoewyk, J. 539 568 Van Loan, C. 385 Van Mechelen, I. 190 439 Variable selection, why we prefer to use hierarchical models 405 Variance as parameterized in Bugs 592 Variance matrices see "Covariance matrices" Vector and matrix notation 5 Vehovar, V. 540 Venables, W. 29 608 Verbeke, G. 494 Verdinelli, L. 28 Vetterling, W. 331 348 584 Vidaillet, H. 238 Volinsky, C. 191 Von Krosigk, C. 411 Wagner, D. 238 Wahba, G. 412 515 Wahba, S. 238 Wakefield, J. 28 152 191 348 515 Wallace, D. 150 457 458 Waller, L. 494 Ware, J. 150 Warnes, G. 609 Wasserman, L. 66 191 457 589 Wasserman, W. 385 Wavelet models 494 Wedderburn, R. 191 439 Weeks, B. 151 439 Weerahandi, S. 190 Weibull distribution 580 Weihs, C. 514 Weisberg, H. 411 Weisberg, S. 385 Weiss, N. 331 Weiss, R. 191 192 Wermuth, N. 151 West, M. 28 412 440 480 494 Whang, W. 569 Wideman, L. 113 539 Wikle, C. 494 Wilcoxon rank test 252 Wild, P. 439 Wilks, A. 29 608 Williams, C. 332 Wilson, D. 348 WinBugs and Bugs 592 Winer, E. 569 Wishart distribution 574 581 Wishart distribution, other models for inverse covariance matrices 483—486 Wolfe, R. 190 Wolpert, R. 28 113 152 Wong, F. 493 Wong, W. 309 331 348 539 Woodbury, M. 66 151 331 Wright, M. 331 385 Wu, A. 238 Wu, Y. 309 331 412 480 Xia, H. 494 Yang, G. 589 Yang, R. 493 Yates, F. 412 York, J. 494 Yu, B. 191 348 Yusuf, S. 151 Zaslavsky, A. 113 440 539 Zeger, S. 230 439 Zelen, M. 239 Zellner, A. 190 385 Zhang, J. 238 Zhao, L. 515 Zhao, X. 238 Zidek, J. 66 Zimmer, W. 29
Реклама
|
|
О проекте