## Probability on Banach spaces |

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### Contents

Submartingale Characterization of Measurable | 69 |

On Semiamarts Amarts and Processes with Finite | 197 |

Geometry and Martingales in Banach Spaces | 267 |

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amart Assume assumption B-convex spaces B-valued Banach space Borel bounded centered Gaussian measure central limit theorem continuous paths converges a.s. convex Corollary cotype q covariance kernel covariance operator define definition denote E(sup equivalent ergodic finite dimensional finitely factorable finitely representable following properties Gaussian measure Gaussian process Hence Hilbert space identically distributed implies independent random inequality infinitesimal triangular array infratype large numbers law of large Lemma Levy measure lim inf lim sup linear log log martingale Math metric space nondecreasing nonnegative normed space Poisy probability space Prop Proposition prove R-type Rademacher random variables random vectors relatively compact relatively shift compact Remark result Riesz decomposition satisfying semiamart sequence of independent stable stationary Gaussian process strong law subset subspace sufficient condition symmetric Theorem 3.1 Theorem II.1.2 uniform convergence weakly zero mean