Corpus ID: 221396866

InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification

@article{Matchev2020InClassNI,
  title={InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification},
  author={Konstantin T. Matchev and Prasanth Shyamsundar},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.00131}
}
We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional independence mixture models (CIMMs). We approach the estimation of a CIMM as a multi-class classification problem, since dividing the dataset into different categories naturally leads to the estimation of the mixture model. InClass nets consist of multiple independent classifier neural networks (NNs), each of which… Expand

References

SHOWING 1-10 OF 79 REFERENCES
Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models
The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models forExpand
Nonparametric inference in multivariate mixtures
We consider mixture models in which the components of data vectors from any given subpopulation are statistically independent, or independent in blocks. We argue that if, under this condition ofExpand
Nonparametric mixture models with conditionally independent multivariate component densities
TLDR
An EM-like algorithm for this model is proposed, and some strategies for selecting the bandwidth matrix involved in the nonparametric estimation step of it are derived. Expand
Estimation of the number of components of nonparametric multivariate finite mixture models
We propose a novel estimator for the number of components (denoted by $M$) in a K-variate non-parametric finite mixture model, where the analyst has repeated observations of $K\geq2$ variables thatExpand
An EM-Like Algorithm for Semi- and Nonparametric Estimation in Multivariate Mixtures
We propose an algorithm for nonparametric estimation for finite mixtures of multivariate random vectors that strongly resembles a true EM algorithm. The vectors are assumed to have independentExpand
NONPARAMETRIC ESTIMATION OF COMPONENT DISTRIBUTIONS IN A MULTIVARIATE MIXTURE
Suppose k-variate data are drawn from a mixture of two distributions, each having independent components. It is desired to estimate the univariate marginal distributions in each of the products, asExpand
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generativeExpand
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and includes detailed algorithms for supervised-learning problem for both regression and classification. Expand
Experiments using machine learning to approximate likelihood ratios for mixture models
Likelihood ratio tests are a key tool in many fields of science. In order to evaluate the likelihood ratio the likelihood function is needed. However, it is common in fields such as High EnergyExpand
Nonparametric Estimation of Multivariate Mixtures
Abstract A multivariate mixture model is determined by three elements: the number of components, the mixing proportions, and the component distributions. Assuming that the number of components isExpand
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