A Bayesian network is a directed acyclic graph (DAG) or a probabilistic graphical model used by statisticians. Vertices of this model represent different variables. Any connections between variables ...

Towards unraveling gene expression models, we consider experimental methods providing discrete RNA counts, with a focus on single-molecule RNA fluorescence in situ hybridization (smFISH 11,12). In ...

Multi-modal molecular profiling data in bulk tumors or single cells are accumulating at a fast pace. There is a great need for developing statistical and computational methods to reveal molecular ...

This manuscript represents a valuable contribution to understanding motion processing in the visual cortex. Based on a heterogeneous collection of previous empirical findings, the authors show that ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...