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Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under(More)
How to estimate the diffusion Ensemble Average Propagator (EAP) from the DWI signals in q-space is an open problem in diffusion MRI field. Many methods were proposed to estimate the Orientation Distribution Function (ODF) that is used to describe the fiber direction. However, ODF is just one of the features of the EAP. Compared with ODF, EAP has the full(More)
Graphical models are increasingly popular tools for modeling problems involving uncertainty. They deal with uncertainty by modeling and reasoning about degrees of uncertainty explicitly based on probability theory. Practical models based on graphical models often reach the size of hundreds of variables. Although a number of ingenious inference algorithms(More)
Monte Carlo sampling has become a major vehicle for approximate inference in Bayesian networks. In this paper, we investigate a fam­ ily of related simulation approaches, known collectively as quasi-Monte Carlo methods based on deterministic low-discrepancy se­ quences. We first outline several theoreti­ cal aspects of deterministic low-discrepancy(More)
Genetic and physiological studies have revealed evidence for multiple signaling pathways by which the plastid exerts retrograde control over photosynthesis associated nuclear genes (PhANGs). It has been proposed that the tetrapyrrole pathway intermediate Mg-protoporphyrin IX (Mg-proto IX) acts as the signaling molecule in the pathways and accumulates in the(More)