Variational BayesDISTRIBUTIONSKey to effective generic, or "black-box," variational inference is the selection of an approximation to the target density that balances accuracy and speed. Copula models are promising options, but calibration of the approximation can be slow for some choices. Smith, ...
Copula变分推理(Copula Variational Inference,CVI)是解决上述问题的主流方法之一,通过使用Copula函数来捕获模型中隐变量之间的依赖关系,虽然可以获得全面且完整的信息,但存在以下问题:(1)由于Copula函数未考虑到依赖关系的稀疏性,从而捕获到一些非必要依赖关系,降低了变分推理近似的准确性;(2)Copula函数采用满秩的方法使得...
Copula variational inference has many advantages: it reduces bias; it is less sensitive to local optima; it is less sensitive to hyperparameters; and it helps characterize and interpret the dependency among the latent variables.doi:10.48550/arXiv.1506.03159Tran, Dustin...
To further address the limitations of traditional VI-BMU methods, this paper introduces a novel Bayesian model updating framework based on variational inference and Gaussian copula model (VGC-BMU). This framework incorporates Gaussian copula model to simulate the dependency relationships between model ...
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the parametric Gaus...
具体工作内容如下:(1)针对CVI中Copula函数忽略隐变量依赖关系的稀疏性和因满秩计算使得复杂度增加的问题,提出一种稀疏Copula变分推理方法(Sparse Copula Variational Inference,SCVI).通过添加稀疏诱导正则化来控制Copula表示的稀疏性,从而找到更加紧凑的表示,去除不重要的依赖关系.具体方法是在Copula参数上添加L1范数来实现...
4678Accesses Metrics Abstract The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence ...
Mean-field variational inference, built on fully factorisations, can be efficiently solved; however, it ignores the dependencies between latent variables, resulting in lower performance. To address this, the copula variational inference (CVI) method is proposed by using the well-established copulas to...
Copula variational inference. In NIPS, 2015.Tran, Dustin, Blei, David, and Airoldi, Edo M. Copula variational inference. In NIPS, pp. 3564-3572, 2015.D. Tran, D. Blei, and E. M. Airoldi. Copula variational inference. In Advances in Neural Information Processing Systems, pages 3564-...
Copula variational inference has many advantages: it reduces bias; it is less sensitive to local optima; it is less sensitive to hyperparameters; and it helps characterize and interpret the dependency among the latent variables.David M. Blei...