Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.

Lean Enterprise Transformation: Measuring and Accelerating the transition to lean.

PowerPoint presentation that showcases: • Research Objectives • Strategic Value of the Lean Enterprise • Multi-Stakeholder Value Optimization • Lean Enterprise Self-Assessment Tool (LESAT) • Leading and Lagging Indicators of Lean Enterprise Transformation • Empirical Results in the Aerospace Industry • Accelerating the Lean Transformation - Linking LESAT to Strategic Objectives • Summary and Questions