Researchers have introduced a statistical model designed to predict disease from microbiome data pooled across many studies, ...

Your institution does not have access to this book on JSTOR. Try searching on JSTOR for other items related to this book. https://www.jstor.org/stable/10.1521/jj ...

A methodological commentary questions the Bayesian modelling, self-reported outcomes and causal interpretations in a recent ...

Authors Christopher B. DuRoss, Scott E.K. Bennett, Richard W. Briggs, Stephen Personius, Ryan D. Gold, Nadine G. Reitman, Adam I. Hiscock, Shannon A. Mahan ...

Researchers explore AI's role in scientific modeling, from historical breakthroughs to modern Bayesian frameworks like ModelSMC.

The linear mixed model (LMM) has emerged as a powerful statistical tool in genetic association studies, enabling researchers to control for population structure and relatedness among study ...

Researchers must often choose between Bayesian or neural network models of behavior, two paradigms with complementary strengths and weaknesses. An ideal paradigm would facilitate testing many kinds of ...

Authors Charles B Yackulic, Maria C Dzul, Jane A Reid, Jamie S. Sanderlin, William M. Block, Joseph L. Ganey, Michael J Dodrill, Mike D Yard ...

As clinical drug development becomes more complex and resource-intensive, the FDA’s recent draft guidance on the use of Bayesian statistical methods in clinical trials signals a move toward more ...

Bayesian modelling portfolio: price elasticity, MCMC, HMMs, GMM/EM, segment conversion, and a deployed Bayesian model with drift monitoring - sdiazricagno/bayesian-modelling ...