KnowraProbability measureLinked fromLinked fromThe 37 pages that link to Probability measure, each with the reason it gives.All 37Broader topic 8Related 14Narrower topic 15Probability density functionNarrower topic: A density represents a probability measure through integration when such a density exists.Cumulative distribution functionNarrower topic: A CDF records the probabilities assigned to events of the form that a variable is at most a given value.Probability mass functionNarrower topic: The mass function represents event probabilities through the values of a discrete variable.Almost everywhereNarrower topic: On probability spaces, almost everywhere is commonly called almost surely.Convergence in distributionNarrower topic: Convergence in distribution is convergence of the probability laws induced by random variables.Empirical distribution functionNarrower topic: The empirical function summarizes a probability measure that places mass on observed values.Risk-neutral measureNarrower topic: A risk-neutral measure is a particular probability measure used for valuation.Portmanteau theoremNarrower topic: The theorem compares probability measures and their mass on measurable sets.Continuous mapping theoremNarrower topic: Distributional convergence compares probability measures induced by the random variables.Lévy's continuity theoremNarrower topic: The theorem identifies its limiting characteristic function with that of a probability measure.Probability axiomsNarrower topic: The axioms are precisely the conditions that make this function a probability measure.Kolmogorov extension theoremNarrower topic: The theorem's conclusion is precisely a probability measure on the product space.Lebesgue decomposition theoremNarrower topic: Probability laws can be decomposed relative to a chosen reference distribution.Girsanov theoremNarrower topic: Girsanov compares two probability measures on the same underlying space.Continuous uniform distributionNarrower topic: The density specifies probabilities by integrating over measurable subsets of the interval.
KnowraProbability measureLinked fromLinked fromThe 37 pages that link to Probability measure, each with the reason it gives.All 37Broader topic 8Related 14Narrower topic 15Probability density functionNarrower topic: A density represents a probability measure through integration when such a density exists.Cumulative distribution functionNarrower topic: A CDF records the probabilities assigned to events of the form that a variable is at most a given value.Probability mass functionNarrower topic: The mass function represents event probabilities through the values of a discrete variable.Almost everywhereNarrower topic: On probability spaces, almost everywhere is commonly called almost surely.Convergence in distributionNarrower topic: Convergence in distribution is convergence of the probability laws induced by random variables.Empirical distribution functionNarrower topic: The empirical function summarizes a probability measure that places mass on observed values.Risk-neutral measureNarrower topic: A risk-neutral measure is a particular probability measure used for valuation.Portmanteau theoremNarrower topic: The theorem compares probability measures and their mass on measurable sets.Continuous mapping theoremNarrower topic: Distributional convergence compares probability measures induced by the random variables.Lévy's continuity theoremNarrower topic: The theorem identifies its limiting characteristic function with that of a probability measure.Probability axiomsNarrower topic: The axioms are precisely the conditions that make this function a probability measure.Kolmogorov extension theoremNarrower topic: The theorem's conclusion is precisely a probability measure on the product space.Lebesgue decomposition theoremNarrower topic: Probability laws can be decomposed relative to a chosen reference distribution.Girsanov theoremNarrower topic: Girsanov compares two probability measures on the same underlying space.Continuous uniform distributionNarrower topic: The density specifies probabilities by integrating over measurable subsets of the interval.