7 papers
From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing
Xinxin Chen, Ezequiel Braga, Joseph G. Ibrahim +2
One of the main advantages of the Bayesian approach to statistical inference is the flexibility in incorporating information from various sources, from expert opinion to historical…
Mapping the landscape of mathematical models for antimicrobial resistance: a scoping review
Felipe Schardong, Claudio Jose Struchiner, Luiz Max Carvalho
Background: Antimicrobial resistance (AMR) is a major global public health problem, contributing to an estimated 4.95 million deaths in 2019 and projected to cause up to 10 million…
hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
Ethan M. Alt, Xinxin Chen, Luiz M. Carvalho +1
There has been increased interest in the use of historical data to formulate informative priors in regression models. While many such priors for incorporating historical data have…
On the lumpability of tree-valued Markov chains
Rodrigo B. Alves, Yuri F. Saporito, Luiz M. Carvalho
Phylogenetic trees constitute an interesting class of objects for stochastic processes due to the non-standard nature of the space they inhabit. In particular, many statistical app…
Embarrassingly Parallel GFlowNets
Tiago da Silva, Luiz Max Carvalho, Amauri Souza +2
GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. Training GFlowNets requires repeated evaluations of the unnormalized target dist…
ANPP: the Adapted Normalized Power Prior for Borrowing Information from Multiple Historical Datasets in Clinical Trials
Yueqi Shen, Matthew A. Psioda, Luiz M. Carvalho +1
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,…
A Comparison of Image and Scalar-Based Approaches in Preconditioner Selection
Michael Souza, Luiz M. Carvalho, Douglas Augusto +3
Within high-performance computing (HPC), solving large sparse linear systems efficiently remains paramount, with iterative methods being the predominant choice. However, the perfor…