Beyond Black Box Densities: Parameter Learning for the Deviated Components. (arXiv:2202.02651v2 [stat.ML] UPDATED)

As we collect additional samples from a data population for which a known
density function estimate may have been previously obtained by a black box
method, the increased complexity of the data set may result in the true density
being deviated from the known estimate by a mixture distribution. To model this
phenomenon, we consider the \emph{deviating mixture model} $(1-\lambda^{*})h_0
+ \lambda^{*} (\sum_{i = 1}^{k} p_{i}^{*} f(x|\theta_{i}^{*}))$, where $h_0$ is
a known density function, while the deviated proportion $\lambda^{*}$ and …

arxiv black box components