Bayesian Networks—Artificial Intelligence for Research, Analytics, and Reasoning


In this workshop, we illustrate how scientists in many fields of study—rather than only computer scientists—can employ Bayesian networks as a very practical form of Artificial Intelligence for exploring complex problems. We present the remarkably simple theory behind Bayesian networks and then demonstrate how to utilize them for research and analytics tasks with the BayesiaLab software platform. More specifically, we explain BayesiaLab's supervised and unsupervised machine learning algorithms for knowledge discovery in high-dimensional domains.

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  • I am still confused about why you took 3^8 for the model with arcs inbound? Can you explain why you used power in model with arc inbound and normal sum with arc outbound?

    • Vaishnavi K In the model on the right, we have eight parent nodes with three states each, one parent node with 2 states, plus the target node with two states, which gives us 3×3×3×3×3×3×3×3+2×2 cells (3^8+2^2) for the CPT of the target node.

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