Sudipto Banerjee,Xiang Chen,Ian Frankenburg et al.
Sudipto Banerjee et al.
We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary ...
Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response [0.03%]
基于部分标注处理和响应的平均处理效应的高效且鲁棒的半监督估计方法
Jue Hou,Rajarshi Mukherjee,Tianxi Cai
Jue Hou
A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clinical variables, including the treatment received and the response. Both treatment informat...
A Framework for Improving the Reliability of Black-box Variational Inference [0.03%]
一种提高黑盒变分推理可靠性的框架
Manushi Welandawe,Michael Riis Andersen,Aki Vehtari et al.
Manushi Welandawe et al.
Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization me...
Rajarshi Guhaniyogi,Laura Baracaldo,Sudipto Banerjee
Rajarshi Guhaniyogi
Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large data applications, primarily due to prohibitively slow posterio...
Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data [0.03%]
异质组数据的贝叶斯多组高斯过程模型
Didong Li,Andrew Jones,Sudipto Banerjee et al.
Didong Li et al.
Gaussian processes are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Scientific data are often heterogeneous in their inputs and contain multiple known discrete groups of ...
Shiwen Zhao,Chuan Gao,Sayan Mukherjee et al.
Shiwen Zhao et al.
Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the ...
Junsouk Choi,Yang Ni
Junsouk Choi
Zero-inflated count data arise in a wide range of scientific areas such as social science, biology, and genomics. Very few causal discovery approaches can adequately account for excessive zeros as well as various features of multivariate co...
DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data [0.03%]
基于分布偏移的分布式因果推断方法研究
Jiayi Tong,Jie Hu,George Hripcsak et al.
Jiayi Tong et al.
High-dimensional healthcare data, such as electronic health records (EHR) data and claims data, present two primary challenges due to the large number of variables and the need to consolidate data from multiple clinical sites. The third key...
Michele Peruzzi,David B Dunson
Michele Peruzzi
Quantifying spatial and/or temporal associations in multivariate geolocated data of different types is achievable via spatial random effects in a Bayesian hierarchical model, but severe computational bottlenecks arise when spatial dependenc...
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data [0.03%]
基于维度分组的混合成员模型在多变量分类数据中的应用
Yuqi Gu,Elena A Erosheva,Gongjun Xu et al.
Yuqi Gu et al.
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizin...