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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We study whether and how can we model a joint distributionp(x,z)using two conditional modelsp(x|z) and q(z|x) that form a cycle.
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On the Generative Utility of Cyclic Conditionals Chang Liu 1, Haoyue T ang 2, Tao Qin
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Work done during an internship at Microsoft Research Asia.
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Information-Theoretic Confidence Bounds for Reinforcement Learning
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