Bayesian methods have significantly enhanced the analysis of complex data structures, such as semicontinuous outcomes and zero-inflated observations, by providing coherent inferences and more ...

Bayesian analysis offers a robust framework for deciphering the intricate dynamics of time series data. By treating unknown parameters as random variables, this approach incorporates prior ...

Antonio R. Linero, Michael J. Daniels, Bayesian Approaches for Missing Not at Random Outcome Data, Statistical Science, Vol. 33, No. 2 (May 2018), pp. 198-213 ...

Efficient variable selection in high-dimensional cancer genomic studies is critical for discovering genes associated with specific cancer types and for predicting response to treatment. Censored ...

Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to ...

To introduce the student to the basic principles and tools of Bayesian Statistics and most importantly to Bayesian data analysis techniques. A major goal is to establish a firm foundation in the ...

GAC Honda Applies for Patent on Vehicle Fault Diagnosis Based on Bayesian Networks, Enhancing Diagnostic Intuitiveness and Interpretability. GAC Honda's Innovative Technology Lead ...

How do you shape perception ethically when storytelling beats facts? Here is a case for ethical storytelling in our current ...

Aerobic exercise interventions for promoting cardiovascular health and mobility after stroke: a systematic review with Bayesian network meta-analysis ...

A Bayesian statistics-based method derives unprecedented exact integral formulas, allowing future applications in various areas, including genetic analysis ...