Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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Collapsing Bandits and Their Application to Public Health Interventions Aditya Mate
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"collapsing" any uncertainty, but when an arm is passive, no observation is made,
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DeepRao-BlackwellisedParticleFiltersforTime SeriesForecasting
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To improve the forecasting capabilities, we extend this classical model by conditionally linear state-to-switch dynamics, while leaving thepartial tractability oftheconditional Gaussian linear part intact.
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Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
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Can't wait to see the second movie! The first two lines are two demonstrations, and the third line is a test input.
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