Covariance Matrix Approximation System
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A Covariance Matrix Approximation System is a Matrix Approximation System (that implements a Covariance Matrix Approximation Algorithm to solve a Covariance Matrix Approximation Task.
- AKA: Covariance Matrix Estimator.
- Context:
- …
- Example(s):
- See: Probabilistic Matrix Factorization, Multivariate Random Variable, Joint Probability Distribution, Sample Covariance Matrix, Unbiased Estimator, Positive-Definite Matrix.
References
2014
- http://scikit-learn.org/stable/modules/covariance.html
- Many statistical problems require at some point the estimation of a population’s covariance matrix, which can be seen as an estimation of data set scatter plot shape. Most of the time, such an estimation has to be done on a sample whose properties (size, structure, homogeneity) has a large influence on the estimation’s quality. The sklearn.covariance package aims at providing tools affording an accurate estimation of a population’s covariance matrix under various settings.
We assume that the observations are independent and identically distributed (i.i.d.).
- Many statistical problems require at some point the estimation of a population’s covariance matrix, which can be seen as an estimation of data set scatter plot shape. Most of the time, such an estimation has to be done on a sample whose properties (size, structure, homogeneity) has a large influence on the estimation’s quality. The sklearn.covariance package aims at providing tools affording an accurate estimation of a population’s covariance matrix under various settings.