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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References
Neural Information Processing Systems
A practical guide to training restricted boltzmann machines.
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FSP-L APLACE: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning Tristan Cinquin
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Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets.
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4620a66570e554a3ff0e39dc59bcb07a-Paper-Conference.pdf
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By design, COLP is a parsimonious classifier, which gives rise to a provably identifiable causal model.
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