## Information Theory, Statistical Decision Functions, Random ProcessesPublishing House of the Czechoslovak Academy of Sciences, 1974 - Probabilities |

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### Contents

Minimax stopping times and games of stopping | 11 |

Parameter estimation for a spatial regression model with stationary | 23 |

Reasoning and derivation of knowledge in probabilistic expert systems | 31 |

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39 other sections not shown

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algorithm applications approximation assume assumption asymptotic bound capacity region characterization CIR(N CIRs Cl-statements coefficients computational conditional independence consider convex corresponding covariance D-rules deductive system defined definition denote depends distribution function element empirical process EMVDs equation estimator exists expert systems exponential finite formula fuzzy Gaussian noise given Hence Hilbert space holds implies index set Information Theory INT(M integral large deviation theory Lemma lim n"1 linear mapping Markov chain martingale matrix matroid observable network obtain oligodistributions one-dependent process optimal p-representable parameter player point process Poisson process probabilistic probability measure problem processors Proof properties Proposition prove quadratic quantile random evolution random variables random vector relation sample satisfying semi-Markov process semi-Markov random sequence SMRE solution stationary stationary policy statistically independent statistics stochastic strategies subset Suppose Theorem topology weak convergence weakly zero