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Development of Statistical Methods Needed
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accuracy added adjusted directions approximation artificial variables assumption AX+V=L basic feasible solution basic solution Chapter coefficients concave functions constant constraint equations converged convex combination convex functions convex set coordinates covariance matrix Creusen curve defined derivative determined developed diagonal matrix direction observations discussion distance measured distance observation equal error ellipse extreme point finite number geodetic control networks given global optimum inverse iteration least squares estimator Linear Equation linear manifold linear programming problem main diagonal elements maximum likelihood estimate minimize minimum variance multivariate normal distribution non-linear programming problem non-negative normal distribution number of pointings objective function Observation Equations obtained off-diagonal elements positive definite probability density function probability function probable error quadratic form random variable restricted basis entry sample problems separable programming set of directions set of feasible simplex algorithm simplex method simply singular solution set solved station adjustment statistical subspace theory triangulation variance-covariance matrix vector yield zero