Company Filing History:
Years Active: 2011-2014
Title: David Koelle: Innovator in Bayesian Belief Networks
Introduction
David Koelle is a notable inventor based in Arlington, MA (US). He has made significant contributions to the field of Bayesian belief networks, holding a total of 3 patents. His work focuses on simplifying causal influence models and enhancing probabilistic inference systems.
Latest Patents
Koelle's latest patents include innovative methods and systems for constructing Bayesian belief networks. These methods aim to simplify causal influence models that describe the influence of parent nodes on the possible states of a child node. The child node and parent nodes are categorized into discrete Boolean, discrete Ordinal, and Categorical nodes. His work also includes the development of user interfaces that incorporate these specific node types. Another significant patent involves conditional probability tables for Bayesian belief networks. This apparatus is designed to make probabilistic inferences based on a belief network, utilizing a processing system to convert parameters of a causal influence model into entries of a conditional probability table.
Career Highlights
David Koelle is currently employed at Charles River Analytics, Inc., where he continues to advance his research and development in Bayesian belief networks. His innovative approaches have positioned him as a key figure in this specialized field.
Collaborations
Some of his notable coworkers include Zachary T Cox and Jonathan Pfautz, who contribute to the collaborative environment at Charles River Analytics, Inc.
Conclusion
David Koelle's work in Bayesian belief networks exemplifies the intersection of innovation and practical application in technology. His contributions continue to influence the field and inspire future advancements.