In the Big Data era, many scientific and engineering domains are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, ...

Approaches for statistical inference -- The Bayes approach -- Bayesian computation -- Model criticism and selection -- The empirical Bayes approach -- Bayesian design -- Special methods and models -- ...

Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 64, No. 4 (AUGUST 2015), pp. 593-610 (18 pages) The paper proposes a fully Bayesian approach for the analysis of triadic ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

When successive randomized trials contradict prior evidence, clinicians may be unsure how to evaluate them: Does accumulating evidence warrant changing practice? An increasingly popular solution, ...

This is a preview. Log in through your library . Abstract Selection of significant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

Scientists have turned to advanced AI to decode the intricate ecosystem of gut bacteria and their chemical signals. Using a Bayesian neural network called VBayesMM, researchers can now identify ...