(2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1):217-240.Hill, J. L. & McCulloch, R. E. (2007), `Bayesian nonparametric modeling for causal inference', Journal of the American Statistical Association. In press....
Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference."Journal of Computational and Graphical Statistics20.1 (2011): 217-240. Hahn, P. Richard, Jared S. Murray, and Carlos M. Carvalho. "Bayesian regression tree models for causal inference: Regularization, confounding, and heteroge...
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in vari... AM Alaa,MVD Schaar - 《IEEE Journal of Selected Topics in...
34 统计最高奖得主【稀疏估计中令人不愉快的性质Unpleasant Properties of Sparse Estimators(e.g. Lasso)】 1:18:54 统计最高奖得主【非参数方法的缺陷Pitfall of Nonparametric Methods 】—Larry Wasserman 1:19:44 统计最高奖得主【非参贝叶斯Nonparametric Bayes】——Larry Wasserman 1:18:24 想了解顶级统计...
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Bayesian modelingcausal inferencenonparametric regressionsensitivity analysisunmeasured confoundingWhen estimating causal effects, unmeasured confounding and model ... Vincent,Dorie,Masataka,... - 《Statistics in Medicine》 被引量: 20发表: 2016年 Bayesian nonparametric models for spatially indexed data of mi...
Here are some of the most important building blocks which are used to construct Bayesian nonparametric models: Gaussian processesare priors over functions such that the values sampled at any finite set of points are jointly Gaussian. In many cases, posterior inference is tractable. This is probably...
Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 2011, 20, 217–240. [Google Scholar] [CrossRef] Chipman, H.A.; George, E.I.; Mcculloch, R.E. BART: Bayesian additive regression trees. Ann. Appl. Stat. 2010, 4, 266–298. [Google Scholar] [CrossRef] ...
A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses. Neuroimage 95, 162–175 (2014). Google Scholar Gorrostieta, C., Fiecas, M., Ombao, H., Burke, E. & Cramer, S. Hierarchical vector auto-regressive models and their ...
Bayesian nonparametric mixture models, exemplified by the Dirichlet process mixture model (DPMM), provide a principled Bayesian approach to adapt model complexity to the data. Dinari et al. [9] used Julia to implement efficient and easily modifiable distributed inference in DPMMs. K -nearest ...