Bayesian posterior approximation via greedy particle optimization

21 May 2018Futoshi FutamiZhenghang CuiIssei SatoMasashi Sugiyama

In Bayesian inference, the posterior distributions are difficult to obtain analytically for complex models such as neural networks. Variational inference usually uses a parametric distribution for approximation, from which we can easily draw samples... (read more)

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