Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Determinants and two-year change in anterior chamber angle width in a Chinese population

Objective: To study the population distribution and longitudinal changes in anterior chamber angle width and its determinants among Chinese adults. Design: Prospective cohort, population-based study. Participants: Persons aged 35 years or more residing in Guangzhou, China, who had not previously undergone incisional or laser eye surgery. Methods: In December 2008 and December 2010, all subjects underwent automated keratometry, and a random 50% sample had anterior segment optical coherence tomography with measurement of angle-opening distance at 500 μm (AOD500), angle recess area (ARA), iris thickness at 750 μm (IT750), iris curvature, pupil diameter, corneal thickness, anterior chamber width (ACW), lens vault (LV), and lens thickness (LT) and measurement of axial length (AL) and anterior chamber depth (ACD) by partial coherence laser interferometry. Main Outcome Measures: Baseline and 2-year change in AOD500 and ARA in the right eye. Results: A total of 745 subjects were present for full biometric testing in both 2008 and 2010 (mean age at baseline, 52.2 years; standard deviation [SD], 11.5 years; 53.7% were female). Test completion rates in 2010 varied from 77.3% (AOD500: 576/745) to 100% (AL). Mean AOD500 decreased from 0.25 mm (SD, 0.13 mm) in 2008 to 0.21 mm (SD, 13 mm) in 2010 (difference, -0.04; 95% confidence interval [CI], -0.05 to -0.03). The ARA decreased from 21.5±3.73 10^-2 mm² to 21.0±3.64 10 ^-2 mm² (difference, -0.46; 95% CI, -0.52 to -0.41). The decrease in both was most pronounced among younger subjects and those with baseline AOD500 in the widest quartile at baseline. The following baseline variables were significantly associated with a greater 2-year decrease in both AOD500 and ARA: deeper ACD, steeper iris curvature, smaller LV, greater ARA, and greater AOD500. By using simple regression models, we could explain 52% to 58% and 93% of variation in baseline AOD500 and ARA, respectively, but only 27% and 16% of variation in 2-year change in AOD500 and ARA, respectively. Conclusions: Younger persons and those with the least crowded anterior chambers at baseline have the largest 2-year decreases in AOD500 and ARA. The ability to predict change in angle width based on demographic and biometric factors is relatively poor, which may have implications for screening. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article. © 2012 American Academy of Ophthalmology.

Slot width augmentation in a slotted-wall transonic linear cascade wind tunnel

Transonic tests in linear cascade wind tunnels can suffer
from significant test section boundary interference effects in pitch. A slotted tailboard has been designed and optimised with an in-house Euler numerical method to reduce such ef- fects. Wind tunnel measurements on an overspeed Mach 1.27 discharge from a Rolls-Royce T2 cascade, featuring strong end-wall shock-induced interference, showed a 77% reduction in the flow pitchwise periodicity error with the optimised tail- board, with respect to the baseline open-jet cascade flow. Two-dimensional Euler predictions were also cross-validated against a three-dimensional Reynolds averaged computation, to explore the three-dimensionality of the discharge

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.