[BOOK][B] Uncertainty quantification: theory, implementation, and applications
RC Smith - 2024 - SIAM
Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …
engineering, and biological applications using mechanistic models. From a broad …
Comparative efficacy and safety of tezepelumab and other biologics in patients with inadequately controlled asthma according to thresholds of type 2 inflammatory …
K Ando, Y Fukuda, A Tanaka, H Sagara - Cells, 2022 - mdpi.com
The anti-thymic stromal lymphopoietin antibody (tezepelumab) has therapeutical potential
for inadequately controlled asthma. However, evidence comparing tezepelumab with other …
for inadequately controlled asthma. However, evidence comparing tezepelumab with other …
Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist.
Bayesian statistical methods are slowly cree** into all fields of science and are becoming
ever more popular in applied research. Although it is very attractive to use Bayesian …
ever more popular in applied research. Although it is very attractive to use Bayesian …
Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study
OBJECTIVE:(1) To generate a list of items that experts consider most important when
reporting a Bayesian analysis of a clinical study,(2) to report on the extent to which we found …
reporting a Bayesian analysis of a clinical study,(2) to report on the extent to which we found …
[BOOK][B] Contemporary Bayesian econometrics and statistics
J Geweke - 2005 - books.google.com
Tools to improve decision making in an imperfect world This publication provides readers
with a thorough understanding of Bayesian analysis that is grounded in the theory of …
with a thorough understanding of Bayesian analysis that is grounded in the theory of …
boa: an R package for MCMC output convergence assessment and posterior inference
BJ Smith - Journal of statistical software, 2007 - jstatsoft.org
Markov chain Monte Carlo (MCMC) is the most widely used method of estimating joint
posterior distributions in Bayesian analysis. The idea of MCMC is to iteratively produce …
posterior distributions in Bayesian analysis. The idea of MCMC is to iteratively produce …
[BOOK][B] Genetic data analysis for plant and animal breeding
We wrote this book to fill the gap between textbooks of quantitative genetic theory and
software manuals that provide details on analytical methods but little context or perspective …
software manuals that provide details on analytical methods but little context or perspective …
Bayesian analysis of mixture models with an unknown number of components-an alternative to reversible jump methods
M Stephens - Annals of statistics, 2000 - JSTOR
Richardson and Green present a method of performing a Bayesian analysis of data from a
finite mixture distribution with an unknown number of components. Their method is a Markov …
finite mixture distribution with an unknown number of components. Their method is a Markov …
[BOOK][B] Bayesian biostatistics
E Lesaffre, AB Lawson - 2012 - books.google.com
The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …
considerable technical innovation in both methodology and computational practicality. One …
Highways of gene sharing in prokaryotes
RG Beiko, TJ Harlow… - Proceedings of the …, 2005 - National Acad Sciences
The extent to which lateral genetic transfer has shaped microbial genomes has major
implications for the emergence of community structures. We have performed a rigorous …
implications for the emergence of community structures. We have performed a rigorous …