168 citations · 467 across the 18 of their papers we have counts for
18 papers
Inferential Models: The Power of Auxiliary Variables for Reasoning with Scientific Uncertainty
Chuanhai Liu
A central challenge in scientific inference is to produce uncertainty assessments that are both situation-specific and frequency-calibrated. This article examines inferential model…
A New Method for Multinomial Inference using Dempster-Shafer Theory
Earl C. Lawrence, Alexander C. Murph, Scott A. Vander Wiel +1
A new method for multinomial inference is proposed by representing the cell probabilities as unordered segments on the unit interval and following Dempster-Shafer (DS) theory. The…
Inferential models and possibility measures
Chuanhai Liu, Ryan Martin
The inferential model (IM) framework produces data-dependent, non-additive degrees of belief about the unknown parameter that are provably valid. The validity property guarantees,…
An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm
Sanvesh Srivastava, Glen DePalma, Chuanhai Liu
The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algori…
Exact and efficient inference for Partial Bayes problems
Yixuan Qiu, Lingsong Zhang, Chuanhai Liu
Bayesian methods are useful for statistical inference. However, real-world problems can be challenging using Bayesian methods when the data analyst has only limited prior knowledge…
Valid uncertainty quantification about the model in a linear regression setting
Ryan Martin, Huiping Xu, Zuoyi Zhang +1
In scientific applications, there often are several competing models that could be fit to the observed data, so quantification of the model uncertainty is of fundamental importance…