# Sculpting distributions with Normalizing Flows

## October 11, 2019

Last posts we’ve investigated Bayesian inference through variational inference (post 1/post 2). In Bayesian inference, we often define models with some unknown model parameters $Z$, or latent stochastic variables $Z$. Given this model and some observed data points $D = \{ D_1, D_2, \dots, D_n \} $, we are interested in the true posterior distribution $P(Z|D)$. This posterior is often intractable and the general idea was to forgo the quest of obtaining the true posterior, but to accept that we are bounded to some easily parameterizable approximate posteriors $^*Q(z)$, which we called variational distributions.
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