Phys.org

Inference of Bayesian networks made fast and easy using an extended depth-first search algorithm

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 ...
Nature

Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data

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 ...
Nature

Gene expression model inference from snapshot RNA data using Bayesian non-parametrics

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 ...
Diginomica

Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

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 ...
Frontiers

Numerical foundations for scalable statistical and machine learning methods

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

Bayesian causal inference unifies perceptual and neuronal processing of center-surround motion in area MT

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 ...