This paper formulates a novel probabilistic graphical model for stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background sources. We present an expectation maximization algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the st...

Gaussian graphical models can capture complex dependency structures among variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate prior information and quantify uncertainty through the posterior distribution. However, computation under conjugate G-Wishart distribution on precision matrix expensive for general nondecomposable graphs. We therefor...

Graphical models are well established in providing compact conditional probability descriptions of complex multivariable interactions. In the Gaussian case, graphical models are determined by zeros in the precision or concentration matrix, i.e. the inverse of the covariance matrix. Hence, there has been much recent interest in sparse precision matrices in areas such as statistics, machine learn...

This study poses the feature correspondence problem as a hypergraph node labeling problem. Candidate feature matches and their subsets (usually of size larger than two) are considered to be the nodes and hyperedges of a hypergraph. A hypergraph labeling algorithm, which models the subset-wise interaction by an undirected graphical model, is applied to label the nodes (feature correspondences) a...

We present a novel approach to detecting and utilizing symmetries in probabilistic graphical models with two main contributions. First, we present a scalable approach to computing generating sets of permutation groups representing the symmetries of graphical models. Second, we introduce orbital Markov chains, a novel family of Markov chains leveraging model symmetries to reduce mixing times. We...

We review and extend the qualitative relationships about the informational relevance of variables in graphical decision models based on conditional independencies revealed through graphical separations of nodes from nodes representing utility on outcomes. We exploit these qualitative relationships to generate non-numerical graphical procedures for identifying partial orderings over chance varia...

In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given data set is not a simple problem, because Markov networks rigorously represent Markov properties, and this rigor imposes complex constraints on the design of the networks. Our proposed model removes these constraints,...

One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use an...

One popular and efficient scheme for solving exactly MPE/MAP and related problems over graphical models is depth-first Branch and Bound. However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down. This paper 1) analyzes and demonstrates this inherent conflict between effective exploitation of problem decomposition (through AND/OR sear...

We present a novel inpainting algorithm for microstructural image data using generative adversarial networks. This enables fast artefact removal via simple graphical user interface.