Bayesian Nonparametric Data Analysis. P. Müller, F. A. Quintana, A. Jara, and T. Hanson (2015). Springer Series in Statistics. New York, NY: Springer. 193 pages, ISBN: 978‐3‐319‐18967‐3.No abstract is available for this article....
1. Longitudinal data are ubiquitous as evidenced by a plethora of recent books covering the topic (Hand and Crowder 1996; Davidian and Giltinan 1998; Verbeke and Molenberghs 2000; Diggle, Heagerty,...
The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the ...
et al. A Bayesian nonparametric approach to super-resolution single-molecule localization. Ann. Appl. Stat. 15, 1742–1766 (2021). Verdaasdonk, J. S., Lawrimore, J. & Bloom, K. in Methods in Cell Biology Vol. 123 (eds Waters, J. C. & Wittman, T.) 347–365 (Elsevier, 2014)....
As outlined above,Bayesian data analysisis based on meaningfully parameterized descriptive models. Are there ever situations in which such models cannot be used or are not wanted? One situation in which it might appear thatparameterized modelsare not used is with so-callednonparametric models. But ...
The users often have additional knowledge when Bayesian nonparametric models (BNP) are employed, e.g. for clustering there may be prior knowledge that some of the data instances should be in the same cluster (must-link constraint) or in different clusters (cannot-link constraint), and similarly...
Texts in Statistical Science(共72册),这套丛书还有 《Problems and Solutions in Biostatistical Theory (Chapman & Hall/Crc Texts in Statistical Science Series)》《Introduction to Probability, Second Edition》《Analysis of Categorical Data with R》《Beyond ANOVA》《An Introduction to Nonparametric Statistics...
Often, it can be easy to see how the induced prior looks through simulation or to perform a sensitivity analysis. In closing, we mention a recent book chapter that parallels the current chapter. Johnson and de Carvalho (2015) discuss a broad array of Nonparametric Bayesian methods that apply ...
Bayesian nonparametric inference for panel count data with an informative observation processdependent frailtyGaussian processHamiltonian Monte Carlononhomogeneous Poisson processrecurrent eventIn this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying ...
We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random...