Multivariate Gaussian approximations on Markov chaoses
classification
🧮 math.PR
keywords
convergencemarkovvectorsadditionanotherapproximationscasechaoses
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We prove a version of the multidimensional Fourth Moment Theorem for chaotic random vectors, in the general context of diffusion Markov generators. In addition to the usual componentwise convergence and unlike the infinite-dimensional Ornstein-Uhlenbeck generator case, another moment-type condition is required to imply joint convergence of of a given sequence of vectors.
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