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A Bayesian Probability Calculus for Density Matrices

A Bayesian Probability Calculus for Density Matrices

This video was recorded at Machine Learning Summer School (MLSS), Taipei 2006. One of the main concepts in quantum physics is a density matrix, which is a symmetric positive definite matrix of trace one. Finite probability distributions can be seen as a special case when the density matrix is restricted to be diagonal. We develop a probability calculus based on these more general distributions that includes definitions of joints conditionals and formulas that relate these, including analogs of the Theorem of Total Probability and various Bayes rules for the calculation of posterior density matrices. The resulting calculus parallels the familiar ``conventional probability calculus and always retains the latter as a special case when all matrices are diagonal.


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