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In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).
The discrepancy function is a continuous function of the elements of S, the sample covariance matrix, and Σ(θ), the "reproduced" estimate of S obtained by using the parameter estimates and the structural model. In order for "maximum likelihood" to meet the first criterion, it is used in a revised form as the deviance.
where ′ + (′) is the variance-covariance matrix implied by the proposed factor analysis model and is the observed variance-covariance matrix. [6] That is, values are found for free model parameters that minimize the difference between the model-implied variance-covariance matrix and observed variance-covariance matrix.
Another design is E-optimality, which maximizes the minimum eigenvalue of the information matrix. S-optimality [9] This criterion maximizes a quantity measuring the mutual column orthogonality of X and the determinant of the information matrix. T-optimality This criterion maximizes the discrepancy between two proposed models at the design ...
In CCA, the model fit, i.e., the discrepancy between the estimated model-implied variance-covariance matrix ^ and its sample counterpart , can be assessed in two non-exclusive ways. On the one hand, measures of fit can be employed; on the other hand, a test for overall model fit can be used.
A Stein discrepancy is a statistical divergence between two probability measures that is rooted in Stein's method.It was first formulated as a tool to assess the quality of Markov chain Monte Carlo samplers, [1] but has since been used in diverse settings in statistics, machine learning and computer science.
C++ template library; binds to optimized BLAS such as the Intel MKL; Includes matrix decompositions, non-linear solvers, and machine learning tooling Eigen: Benoît Jacob C++ 2008 3.4.0 / 08.2021 Free MPL2: Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. Fastor [5]
In mathematical notations, the generalization runs as follows: For each t in T, let L(t) be the m × n matrix of elements of X whose i - th row is ( (t i t)x 1, (t i t)x 2, …, (t i t)x n) for i = 1, …, m. and let R(t) be the m × n matrix of elements of X, the elements of whose j - th column are