computer modeling

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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New research shows link between agrochemicals and risk of parasitic disease

Farmers worldwide face mounting pressure to increase agricultural yields to keep up with human population growth. Consequently, chemical use is on the rise – in many cases a cocktail of chemicals, from fertilizers to herbicides to insecticides. But in countries where human population growth is highest, including China, South East Asia and South America, these […]

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