Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition TimesDependent Dirichlet processGaussian processG-ComputationInverse probability of treatment weightingMarkov chain Monte CarloWe analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for ...
Traditionally, performing full Bayesian inference in Gaussian processes has been prohibitive, with computation scaling as\({\cal{O}}(N^3)\), withNthe number of training data points. However, recent advances in approximate inference methods based on sparse collections of\(M \ll N\)inducing point...
We need the MCMC methods of Sections 3 Algorithm for posterior estimates, 4 Simulation of the trajectories of an NGG process, 5 Inference for functionals to compute the nonparametric Bayesian estimates, whereas efficient computation of the Bayes factors is achieved by the different algorithm of ...
Approximate Bayesian Computation: A Nonparametric Perspective. Jour- nal of the American Statistical Association, 105(491):1178-1187, 2010.BLUM, M. G. (2010). Approximate Bayesian computation: a nonparametric perspective. Journal of the American Statistical Association 105, 1178-1187....
Bayesian estimation and inference for generalised partial linear models using shape-restricted splines 机译:基于形状限制样条的广义局部线性模型的贝叶斯估计和推断 Mary C. Meyer,Amber J. Hackstadt,Jennifer A. Hoeting, Journal of nonparametric statistics 2011 原文传递 原文传递并翻译 示例 加入购物车...
Bayesian Model Robustness via Disparities Jiang W, Tanner MA (2008) Gibbs posterior for variable selection in high-dimensional classification and data mining. Ann Stat 26(5):2207–2231 CrossRef... G Hooker,AN Vidyashankar - 《Test》 被引量: 24发表: 2014年 Asymptotic properties in partial line...
Lawson AB: Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Series: Interdisciplinary Statistics. 2009, New York: Chapman & Hall/CRC Google Scholar Efron B, Morris C: Stein's estimaton rule and its competitors - an empirical Bayes approach. Journal of the American Statistic...
The number of change-points is determined by the Bayesian information criterion and the locations of the change-points can be estimated via the dynamic programming algorithm and the use of the intrinsic order structure of the likelihood function. Under some mild conditions, we show that the new ...
(2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B, 64, 583–640. Walker, S.G. (2007). Sampling the Dirichlet Mixture Model with Slices. Communications in Statistics. Simulation and Computation, 36, 45–54. Wang, F. and Gelfand, A....
Wiley, New YorkKohn, R., Smith, M., and Yau, P. (2000) Nonparametric Bayesian bivariate surface estimation. Chapter 19, 545- 580, in Smoothing and Regression Approaches, Computation and Estimation. Edited by Michael G. Schimek, John Wiley and Sons....