Debarun Bhattacharjya, Research Staff Member IBM Research

Who. What. Why. New IBM algorithm models how the order of prior actions impacts events

To address the problem of ordinal impacts, our team at IBM T. J. Watson Research Center has developed OGEMs – or Ordinal Graphical Event Models – new dynamic, probabilistic graphical models for events. These models are part of the broader family of statistical and causal models called graphical event models (GEMs) that represent temporal relations where the dynamics are governed by a multivariate point process.

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Deriving Complex Insights from Event-driven Continuous Time Bayesian Networks

Real-world decision making often involves situations and systems whose uncertain and inter-dependent variables interact in a complex and dynamic way. Additionally, many scenarios are influenced by external events that affect how system variables evolve. To address these complex scenarios for decision making, together with colleagues at the IBM T. J. Watson Research Center, we have developed a new dynamic, probabilistic graphical model called - Event-driven Continuous Time Bayesian Networks.

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