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 \({\cal{O}}(N^3)\), with N the number of training data points. However, recent advances in approximate inference methods based on sparse collections of \(M \ll N\) induci...
(2021), ‘Bayesian Inverse Regression for Vascular Magnetic Resonance Fingerprinting’, IEEE Transactions on Medical Imaging, 40(7), 1827–1837. (Open in a new window)PubMed(Open in a new window)Google Scholar Celeux, G., Frühwirth-Schnatter, S., and Robert, C.P. (2019), ‘Model ...
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. ...
Our aim is the computation of Bayesian inferences for GP through its posterior distribution via the Gibbs sampler outlined in Section 3. The only step there where the simulation of P (and its functionals) is involved is step a. We will be essentially interested in three particular functionals:...
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 ...
(1996). Bayesian Learning for Neural Networks, New York: Springer-Verlag. Book MATH Google Scholar Richardson, S., and Green, P. (1997). Modelling and Computation for Mixture Problems (with discussion) Journal of the Royal Statistical Society, B, 59, 731–792. Article MathSciNet MATH ...
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...
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, ...