Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition TimesDependent Dirichlet processGaussian processG-ComputationInverse probability of treatment weightingMarkov chain Mont
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...
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....
Tank, A., Foti, N., Fox, E.: Streaming variational inference for Bayesian nonparametric mixture models. In: Lebanon, G., Vishwanathan, S.V.N. (eds.) Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. ...
Wilson, Andrew GPoczos, BarnabasSchneider, JeffXing, Eric PJ. Oliva, A. Dubey, B. Poczos, J. Schneider, and E. P. Xing. Bayesian Non- parametric Kernel-Learning. arXiv:1506.08776 [stat], June 2015. URL http: //arxiv.org/abs/1506.08776. arXiv: 1506.08776....
For this purpose, we propose a nonparametric hierarchical Bayesian model that improves on existing collaborative factorization models and frames a large number of relational learning problems. The proposed model naturally incorporates (co)-clustering and prediction analysis in a single unified framework, ...
crittype may be one of the following: cv (cross-validation), gcv (generalized cross- validation), aic (Akaike's information criterion), bic (Schwarz's Bayesian information criterion), or mallows (Mallows's ). The default is criterion(cv). knots(#) specifies that a piecewise polynomial ...
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...
Beal, M.J., and Ghahramani, Z. (2003), ‘The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures’,Bayesian Statistics, 7(453-464), 210. Google Scholar Biernacki, C., Celeux, G., and Govaert, G. (2000), ‘Assessing a Mixture Mo...
In contrast to these methods, we adopt a Bayesian approach, where seg- mentation is inferred via the Maximum A Posteriori (MAP) principle and the joint label and intensity image distribution is estimated in a nonparametric fashion. The transformations between the test image and each training image...