Practical Nonparametric and Semiparametric Bayesian Statistics
Springer-Verlag New York Inc.
978-0-387-98517-6 (ISBN)
I Dirichlet and Related Processes.- 1 Computing Nonparametric Hierarchical Models.- 2 Computational Methods for Mixture of Dirichlet Process Models.- 3 Nonparametric Bayes Methods Using Predictive Updating.- 4 Dynamic Display of Changing Posterior in Bayesian Survival Analysis.- 5 Semiparametric Bayesian Methods for Random Effects Models.- 6 Nonparametric Bayesian Group Sequential Design.- II Modeling Random Functions.- 7 Wavelet-Based Nonparametric Bayes Methods.- 8 Nonparametric Estimation of Irregular Functions with Independent or Autocorrelated Errors.- 9 Feedforward Neural Networks for Nonparametric Regression.- III Levy and Related Processes.- 10 Survival Analysis Using Semiparametric Bayesian Methods.- 11 Bayesian Nonparametric and Covariate Analysis of Failure Time Data.- 12 Simulation of Lévy Random Fields.- 13 Sampling Methods for Bayesian Nonparametric Inference Involving Stochastic Processes.- 14 Curve and Surface Estimation Using Dynamic Step Functions.- IV Prior Elicitation and Asymptotic Properties 15 Prior Elicitation for Semiparametric Bayesian Survival Analysis.- 16 Asymptotic Properties of Nonparametric Bayesian Procedures.- 17 Modeling Travel Demand in Portland, Oregon.- 18 Semiparametric PK/PD Models.- 19 A Bayesian Model for Fatigue Crack Growth.- 20 A Semiparametric Model for Labor Earnings Dynamics.
| Reihe/Serie | Lecture Notes in Statistics ; 133 |
|---|---|
| Zusatzinfo | XVI, 392 p. |
| Verlagsort | New York, NY |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| ISBN-10 | 0-387-98517-4 / 0387985174 |
| ISBN-13 | 978-0-387-98517-6 / 9780387985176 |
| Zustand | Neuware |
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