Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Building on a strong foundation of philosophy, theory, methods and computation over the
past three decades, Bayesian approaches are now an integral part of the toolkit for most …
past three decades, Bayesian approaches are now an integral part of the toolkit for most …
Optimal priors for the discounting parameter of the normalized power prior
The power prior is a popular class of informative priors for incorporating information from
historical data. It involves raising the likelihood for the historical data to a power, which acts …
historical data. It involves raising the likelihood for the historical data to a power, which acts …
Exploring the Connection Between the Normalized Power Prior and Bayesian Hierarchical Models
The power prior is a popular class of informative priors for incorporating information from
historical data. It involves raising the likelihood for the historical data to a power, which acts …
historical data. It involves raising the likelihood for the historical data to a power, which acts …
Bayesian Detection of Bias in Peremptory Challenges Using Historical Strike Data
United States law bars using peremptory strikes during jury selection because of prospective
juror race, ethnicity, sex, or membership in certain other cognizable classes. Here, we …
juror race, ethnicity, sex, or membership in certain other cognizable classes. Here, we …
Distributed Fractional Bayesian Learning for Adaptive Optimization
This paper considers a distributed adaptive optimization problem, where all agents only
have access to their local cost functions with a common unknown parameter, whereas they …
have access to their local cost functions with a common unknown parameter, whereas they …
On efficient posterior inference in normalized power prior Bayesian analysis
The power prior has been widely used to discount the amount of information borrowed from
historical data in the design and analysis of clinical trials. It is realized by raising the …
historical data in the design and analysis of clinical trials. It is realized by raising the …
Alone, together: On the benefits of Bayesian borrowing in a meta‐analytic setting
It is common practice to use hierarchical Bayesian model for the informing of a pediatric
randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction …
randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction …
Flexible, efficient borrowing: A power prior structure for Bayesian interim analysis
VRC Sieck, FGW Christensen - Quality Engineering, 2023 - Taylor & Francis
When making decisions about whether a product can meet required performance standards,
it is often of interest to make decisions as soon as enough information has been obtained …
it is often of interest to make decisions as soon as enough information has been obtained …
Discussion of specifying prior distributions in reliability applications
S Kulathinal - Applied Stochastic Models in Business and …, 2024 - Wiley Online Library
Tian et al. have reviewed and discussed various noninformative or weakly informative priors
when reliability data are modeled using the log‐location‐scale family of distributions. They …
when reliability data are modeled using the log‐location‐scale family of distributions. They …
Incorporating Historical Information in Bayesian Clinical Trial Design Using the Normalized Power Prior
Y Shen - 2024 - cdr.lib.unc.edu
The power prior is a popular class of informative priors for incorporating information from
historical data. It involves raising the likelihood for the historical data to a power, which acts …
historical data. It involves raising the likelihood for the historical data to a power, which acts …