In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

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 information ...

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...

Functional safety engineers follow the ISA/IEC 61511 standard and perform calculations based on random hardware failures. These result in very low failure probabilities, which are then combined with ...

The increasing interest in Bayesian group sequential design is due to its potential to reinforce efficiency in clinical trials, shorten drug development time, and enhance the accuracy of statistical ...

Sankhyā: The Indian Journal of Statistics, Series B (1960-2002), Vol. 63, No. 3 (Dec., 2001), pp. 270-285 (16 pages) In this paper we present a Bayesian analysis of 2 × 2 contingency tables, ...

We present a novel approach to the empirical Bayes analysis of aggregated survival data from different groups of subjects. The method is based on a contingency table representation of the data and ...

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American I’m not sure when I first heard of Bayes’ ...

A new study using Bayesian network analysis from the NESDA cohort suggests that depressive symptoms, especially those related ...