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A series of lightning talks will be presented highlighting collaborative efforts between UC San Diego and MathWorks via series of supported research and curriculum projects. Join us to learn how each project team are crossing boundaries and using MATLAB & Simulink in their work in areas such as Data Science, Oceanography, and various Engineering disciplines!
A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss innovations in variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning and Bayesian statistics.