Gaussian Processes for Machine Learning
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning , the MIT Press, 2006,ISBN 2006 Massachusetts Institute of AMathematical Joint, Marginal and Conditional ProbabilityLet then(discrete or continuous) random variablesy1,...,ynhave ajointjoint probabilityprobabilityp(y1,...,yn), orp(y) for , one ought to distin-guish between probabilities (for discrete variables) and probability densities forcontinuous variables. Throughout the book we commonly use the term prob-ability to refer to both. Let us partition the variables inyinto two groups,yAandyB, whereAandBare two disjoint sets whose union is the set{1,...,n},so thatp(y) =p(yA,yB). Each group may contain one or more ofyAis given bymarginal probabilityp(yA) = p(yA,yB)dyB.
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. 2006 Massachusetts Institute of Technology.c www ...
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CONTINUOUS QUALITY IMPROVEMENT, Joint probability, Random variables, CORRELATION AND REGRESSION, CORRELATION AND REGRESSION Correlation and regression, Variables, Stochastic Process, Random, Random Variables and Probability Distributions, Continuous Random Variables, Continuous, Signals and LTI Systems