Nature

Bayesian Quantile Regression and Statistical Modelling

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...
JSTOR Daily

Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols Journal of the ...
JSTOR Daily

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
GlobalData on MSN

The Bayesian challenge: complexity that pays off

The FDA has released draft guidance on how sponsors can use Bayesian models for clinical trials.
ascopubs.org

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation

The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived ...
lse

Bayesian Data Analysis

This course is available on the MPA in Data Science for Public Policy, MSc in Data Science, MSc in Health Data Science, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics ...