Multivariate transfer entropy (TE) is a model-free approach to detect causalities in multivariate time series. It is able to distinguish direct from indirect causality and common drivers without assuming any underlying model. But despite these advantages it has mostly been applied in a bivariate setting as it is hard to estimate reliably in high dimensions since its definition involves infinite...

This paper introduces a new approximate inference algorithm for credal networks. The algorithm consists of two major steps. It starts by representing the credal network as a compiled logical theory. The resulting graphical structure is the basis on which the subsequent steepest-ascent hill-climbing algorithm operates. The output of the algorithm is an inner approximation of the exact lower and ...

Regulation of neural protein kinase C (PKC) activity appears to directly affect the persistence of long-term potentiation (LTP; Akers and Routtenberg, 1985; Lovinger et al., 1985, 1986, 1987; Routtenberg et al., 1985, 1986; Akers et al., 1986; Linden et al., 1987), a model of neural plasticity (Bliss and Lomo, 1973). In addition, the in vitro phosphorylation of a brain-specific PKC substrate, p...

We formulate the weighted b-matching objective function as a probability distribution function and prove that belief propagation (BP) on its graphical model converges to the optimum. Standard BP on our graphical model cannot be computed in polynomial time, but we introduce an algebraic method to circumvent the combinatorial message updates. Empirically, the resulting algorithm is on average fas...

We describe an algorithm for approximate inference in graphical models based on Hölder’s inequality that provides upper and lower bounds on common summation problems such as computing the partition function or probability of evidence in a graphical model. Our algorithm unifies and extends several existing approaches, including variable elimination techniques such as minibucket elimination and v...

UNLABELLED Computing the probability of identity by descent sharing among n genes given only the pedigree of those genes is a computationally challenging problem, if n or the pedigree size is large. Here, I present a novel graphical algorithm for efficiently computing all generalized kinship coefficients for n genes. The graphical description transforms the problem from doing many recursion on ...

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...

Probabilistic tracking algorithms typically rely on graphical models based on the first-order Markov assumption. Although such linear structure models are simple and reasonable, it is not appropriate for persistent tracking since temporal failures by short-term occlusion, shot changes, and appearance changes may impair the remaining frames significantly. More general graphical models may be use...

This paper presents design patterns that will help in the task of parallelizing graphical real time algorithms, according to the example of a visual real time 3D reconstruction algorithm. These algorithms can often be designed as a dataflow graph, so they can be coarsely granular parallelized in a pipeline pattern. With these patterns, the design process of the parallelization is detached from ...