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Density Estimation via Bayesian Inference Engines
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation.
The Inverse G-Wishart Distribution and Variational Message Passing
Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models.
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