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- W29041363 abstract "When we consider graphical models for the multivariate complex normal distribution, we formulate the models in terms of simple undirected graphs, which illustrate conditional independence of complex random vectors. Therefore this chapter concentrates on conditional independence of complex random vectors. Conditional independence is studied formally by Dawid (1979), but has also been explored by others see e.g. Pearl (1988). We have chosen only to consider complex random vectors with continuous density function w.r.t. Lebesgue measure, since we only consider such complex random vectors in this book. Besides an acquaintance with Lebesgue measure no further measure theory is used. We define the conditional density function and the conditional distribution for complex random vectors. Then the conditional distribution of a measurable transformation of a complex random vector is defined. Using these definitions we are able to state the law of total probability and further to define conditional independence of two measurable transformations of a complex random vector given a third complex random vector. We establish some properties, which are equivalent to the definition of conditional independence, and we find properties which can be used to deduce conditional independences from others. Furthermore we study conditional independence in the special case, where a complex random vector is partitioned and we give useful theorems in this case. Next conditional independence in relation to simple undirected graphs is studied. Three different ways are used to specify conditional independence by means of a graph. This means that the distributions fulfilling these different conditional independence criteria may have different properties. These are the so called Markov properties." @default.
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- W29041363 date "1995-01-01" @default.
- W29041363 modified "2023-09-24" @default.
- W29041363 title "Conditional Independence and Markov Properties" @default.
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- W29041363 doi "https://doi.org/10.1007/978-1-4612-4240-6_6" @default.
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