Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Identifying gene regulatory network rewiring using latent differential graphical models

Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a...

Inferring the Interactions in Complex Manufacturing Processes Using Graphical Models

Efficient sampling of Gaussian graphical models using conditional Bayes factors

Bayesian estimation of Gaussian graphical models has proven to be challenging because the conjugate prior distribution on the Gaussian precision matrix, the G-Wishart distribution, has a doubly intractable partition function. Recent developments provide a direct way to sample from the G-Wishart distribution, which allows for more efficient algorithms for model selection than previously possible...

Identifying factors that predict persistent driving after drinking, unsafe driving after drinking, and driving after using cannabis among young adults.

UNLABELLED The main aim of this study was to identify adolescent/young adulthood factors that predicted persistent driving after drinking, persistent unsafe driving after drinking, and persistent cannabis use and driving among young adults. It was a longitudinal study of a birth cohort (n=933, 474 males and 459 females) and was based on data collected at ages 15, 18, 21 and 26 years. At each of...

Graphical Models for Composable Finite Markov Processes

Composable Markov processes were introduced by Schweder (1970) in order to capture the idea that a process can be composed of different components where some of these only depend on a subset of the other components. Here we propose a graphical representation of this kind of dependence which has been called ‘local dependence’. It is shown that the graph allows to read off further independencies ...