Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

Seeing How it Sounds: Observation, Imitation and Improved Learning in Piano Playing

Research in various fields has shown that students benefit from teacher action demonstrations during instruction, establishing the need to better understand the effectiveness of different demonstration types across student proficiency levels. This study centres upon a piano learning and teaching environment in which beginners and intermediate piano students (N=48) learning to perform a specific type of staccato were submitted to three different (group exclusive) teaching conditions: audio-only demonstration of the musical task; observation of the teacher's action demonstration followed by student imitation (blockedobservation); and observation of the teacher's action demonstration whilst alternating imitation of the task with the teacher's performance (interleaved-observation). Learning was measured in relation to students' range of wrist amplitude (RWA) and ratio of sound and inter-sound duration (SIDR) before, during and after training. Observation and imitation of the teacher’s action demonstrations had a beneficial effect on students' staccato knowledge retention at different times after training: students submitted to interleaved-observation presented significantly shorter note duration and larger wrist rotation, and as such, were more proficient at the learned technique in each of the lesson and retention tests than students in the other learning conditions. There were no significant differences in performance or retention for students of different proficiency levels. These findings have relevant implications for instrumental music pedagogy and other contexts where embodied action is an essential aspect of the learning process.