Linked from

The 37 pages that link to Probability measure, each with the reason it gives.

• Random variableRelated: It determines the probabilities induced on the random variable's values.

• Probability theoryBroader topic: It encodes likelihoods and obeys the theory’s fundamental consistency rules.

• Probability distributionRelated: Distributions inherit their probabilities from a measure on the variable’s outcomes.

• Probability density functionNarrower topic: A density represents a probability measure through integration when such a density exists.

• Conditional probabilityRelated: The conditional formula starts with this measure and rescales it on the condition.

• Measure theoryBroader topic: Probability theory is measure theory applied to random events.

• Probability spaceRelated: It assigns probabilities to the events in the sigma-algebra.

• 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.

• Sample spaceRelated: It assigns probabilities to subsets of the sample space.

• Event (probability theory)Related: It assigns probabilities to events in the model.

• Almost everywhereNarrower topic: On probability spaces, almost everywhere is commonly called almost surely.

• Convergence in probabilityRelated: The definition asks whether events of large error have probabilities tending to zero.

• Convergence in distributionNarrower topic: Convergence in distribution is convergence of the probability laws induced by random variables.

• Countable additivityBroader topic: Its countable additivity is the axiom that makes probabilities of disjoint alternatives sum.

• Empirical distribution functionNarrower topic: The empirical function summarizes a probability measure that places mass on observed values.

• Borel measureBroader topic: When the Borel space has total mass one, its Borel measure models a random outcome.

• 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.

• Support (mathematics)Broader topic: Its support is the smallest closed set on which the full probability is concentrated.

• Prokhorov's theoremBroader topic: The theorem concerns families made up specifically of probability measures.

• Riesz–Markov–Kakutani representation theoremBroader topic: On compact spaces, integration against probability measures corresponds to positive functionals taking the constant one to one.

• 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.

• Slutsky's theoremRelated: Convergence in probability is defined through probabilities assigned to error events.

• Carathéodory's extension theoremRelated: Probability laws can be constructed by extending prescribed values on a generating family.

• Extended real number lineRelated: Expectation can be an extended real value when a random variable is unbounded.

• Hermite–Hadamard inequalityRelated: Uniform probability on an interval turns the integral average into an expectation.

• 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.

• Outcome (probability)Related: It assigns probabilities to sets of outcomes rather than necessarily to individual ones.

• Continuous uniform distributionNarrower topic: The density specifies probabilities by integrating over measurable subsets of the interval.

• Cox's theoremBroader topic: This is the mathematical form Cox's theorem obtains under its assumptions.

• Dynkin systemRelated: Equality of probability measures can be extended from a generating pi-system using Dynkin-system arguments.

• De Finetti's theoremRelated: The mixing distribution assigns weights to the possible conditional probability laws.