Graphical Models and Bayesian Networks Publication Trend The graph below shows the total number of publications each year in Graphical Models and Bayesian Networks.

This article considers a graphical model for ordinal variables, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian ...

A five-minute formula from Alexander Denev that takes you through a simple probabilistic graphical model and explains how and why these are used. Find out more about the ground-breaking book, ...

We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead to a sparse and shrinkage estimator of the concentration matrix that ...

Description Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical ...

In a paper published in National Science Review, the team of Pro. Liu present an innovative computational framework, the sample-perturbed Gaussian graphical model (sPGGM), designed to analyse ...

Figure 1. Schematic illustration of sample-perturbed Gaussian graphical model (sPGGM) for identifying pre-disease stages. (IMAGE) Science China Press ...

Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables.