Inference in Temporal Models

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From the course by Stanford University
Probabilistic Graphical Models 2: Inference
192 ratings
Stanford University

Probabilistic Graphical Models 2: Inference

192 ratings
Course 2 of 3 in the Specialization Probabilistic Graphical Models
From the lesson
Inference in Temporal Models
In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks.

Meet the Instructors

  • Daphne Koller
    Daphne Koller
    Professor
    School of Engineering
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