Feb 5, 2022 · The increased complexity of the data set may result in the true density being deviated from the known estimate by a mixture distribution.

The increased complexity of the data set may result in the true density being deviated from the known estimate by a mixture distribution.

The primary contribution of this paper is a rich theory of identifiability and rates of convergence for parameters and density estimation that arise in the ...

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, ...

Oct 31, 2022 · We propose the "deviating mixture model" and study its theoretical properties.

Feb 5, 2022 · The primary contribution of this paper is a rich theory of identifiability and rates of convergence for parameters and density estimation that ...

The primary contribution of this paper is a rich theory of identifiability and rates of convergence for parameters and density estimation that arise in the ...

Using a novel notion of distinguishability between the known density h0 and the deviated mixture distribution, we establish rates of convergence for the maximum ...

Beyond Black Box Densities: Parameter Learning for the Deviated Components ... parameter learning rates that arise from a standard maximum likelihood estimation ...

Beyond Black Box Densities: Parameter Learning for the Deviated Components ... satisfies assumption A3. 202. 3.2 Parameter learning rates of deviated components.