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.

How well do elderly people complete individualised quality of life measures: an exploratory study.

This research note describes and discusses a study which investigated the feasibility of using an individualised approach to measure the quality of life (QoL) of a sample of older people who were in receipt of an early hospital discharge service. Most participants (86%) were able to identify areas of their lives which were important to them, rate their level of functioning on each of these areas and rank their life areas in order of importance. However, 39% were unable to quantify the relative importance of each area of life. Indeed, the majority (57%) of participants who were over 75 years old could not complete this “weighting” or evaluative stage. The results suggest that the phenomenological approach to measuring QoL may be employed successfully with older people but that the “weighting” system used by existing individualised QoL measures needs to be refined, especially when assessing people over 75.