and P. Li, High-dimensional Bayesian inference in nonparamet- ric additive models. Electronic Journal of Statistics, 2014. 8: p. 2804-2847.Z. Shang and P. Li, "High-dimensional Bayesian inference in nonparametric additive models," Electronic Journal of Statistics, vol. 8, no. 2, pp. ...
Gaussian graphical modelhigh‐dimensional datainverse‐Wishart distributionDespite major methodological developments, Bayesian inference for Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and ...
High dimensional bayesian inference for gaussian directed acyclic graph models. Technical Report, http://arxiv.org/abs/1109.4371, 2016.Ben-David, E., Li, T., Massam, H., Rajaratnam, B., 2015. High dimensional Bayesian inference for Gaussian directed acyclic graph models. arXiv: 1109.4371 v5...
研究点推荐 high dimensional Bayesian inference Inference functions 站内活动 0 关于我们 百度学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们保持学习的态度,不忘初心,砥砺前行。了解更多>> ...
Inference in the presence of sensory and mental transformation noise (MTN). a Observer models. Top: no inference. The observer makes a noisy measurement (m) of a sensory stimulus (s), applies a noisy mental transformation to compute t, and aims to produce t through a noisy motor system re...
Bayesian inference is a conscientious statistical method which is successfully used in many branches of physics and engineering. Compared to conventional approaches, it makes highly efficient use of information hidden in a measured quantity by predicting
第14,15讨论的是传统的Causal inference的概念,PSW和overlapping在Bayesian 中的implementation。 第17,18是用Bayesian Graph或者network为工具做Causal inference。这里的图一般是DAG,因为DAG中节点有明确的parents,可以用来表示变量和变量之间的因果关系,这也意味着在设计prior的时候我们需要一个能做parents selection的prior...
Hierarchical Bayesian modelling provides an effective Swiss army knife for the analysis of single-cell data because of its ability to decompose variance and quantify uncertainty even in the face of sparse, high-dimensional data. Not surprisingly, Bayesian models are being developed in a variety of ...
This is an R package to implement methods seen in "A Bayesian semiparametric framework for causal inference in high-dimensional data" by Joseph Antonelli and Francesca Dominici, which can be found at the following link: https://arxiv.org/pdf/1805.04899.pdf ...
Bayesian Inference for a New Class of Distributions on Equivalence Classes of Three-Dimensional Orientations With Applications to Materials Science: Techno... Vardeman, Bayesian inference for a new class of distributions on equivalence classes of 3-D orientations with applications to materials science,...