Probability theory and stochastic processes Proof
Proof of the monotonicity of probability
Builds on Probability spaceProbability measureEventSubsetSigma-algebraProbability of the complement
Proof
- Let be a probability space and let be events with a subset of .
- The set is in , because a σ-algebra is closed under complements and finite intersections.
- By the complement rule applied to the event , .
- Let , and for . These sets are pairwise disjoint, because no point of lies in .
- Their union is . This equals , because .
- By the countable additivity of the probability measure and step 3, .
- All three numbers are real. So .
- The measure takes values in , so . Hence .
Idea
When , the event splits into and the part of outside . Additivity gives the probability of as the probability of plus a nonnegative remainder.
Reading path
To understand this page from the ground up, read these 52 pages in order. The foundations come first. The path takes the proofs Proof of the approximation property of the supremum, Proof of the probability of the complement, Proof that the reals form an ordered field, with the Cauchy sequence construction, Proof that the reals are Archimedean from the completeness axiom.
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Real numbers object
- Set bounded above definition
- Set definition
- Ring of integers statement
- Archimedean ordered field definition
- Disjunction definition
- Implication definition
- Predicate definition
- The reals form a field statement
- Supremum definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Universal quantifier definition
- Completeness axiom statement
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Binary relation definition
- Universal set definition
- Order on the reals definition
- Power set definition
- Antisymmetric relation definition
- Reflexive relation definition
- Transitive relation definition
- Complement definition
- Interval definition
- Preorder definition
- Sigma-algebra definition
- Partial order definition
- Event definition
- Total order definition
- Probability axioms concept
- Probability measure definition
- Proof that the reals form an ordered field, with the Cauchy sequence construction proof
- Probability space definition
- The reals form an ordered field statement
- Proof of the approximation property of the supremum proof
- Approximation property of the supremum statement
- Proof that the reals are Archimedean from the completeness axiom proof
- The reals are Archimedean statement
- Proof of the probability of the complement proof
- Probability of the complement statement
- Proof of the monotonicity of probability proof
Where this leads
90 pages build on this one.
- Chernoff bound
- Chebyshev's inequality
- Characteristic function of a sum of independent random variables
- Law of total variance
- Cauchy–Schwarz inequality for expectations
- Monotonicity of probability
- Markov's inequality
- De Moivre–Laplace theorem
- Kolmogorov's zero–one law
- Weak law of large numbers
- Multiplication rule
- Addition rule for two events
- Mean of the geometric distribution
- Strong law of large numbers
- Boole's inequality
- Convergence in probability implies convergence in distribution
- Central limit theorem
- The cumulative distribution function determines the distribution
- Coupon collector's problem
- Second Borel–Cantelli lemma
- Sum of independent normal random variables
- Cumulant generating function
- Limit of the cumulative distribution function at infinity
- Almost sure convergence implies convergence in probability
- First Borel–Cantelli lemma
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Continuity of probability measures
- Monotonicity of the cumulative distribution function
- Tail sum formula for expectation
- Sample average approximation
- Cramér–Rao bound
- Efficiency of an estimator
- Ergodic decomposition
- Proof of the tail sum formula for expectation by dyadic approximation
- Koksma–Hlawka inequality
- Proof of the monotonicity of the cumulative distribution function
- Doob's martingale inequality
- Proof of the continuity of probability measures
- Consistency of the maximum likelihood estimator
- Proof of the law of total variance
- Asymptotic equipartition property
- Quantum Fisher information
- Source–channel separation theorem
- Proof of the limit of the cumulative distribution function at infinity
- Proof of Boole's inequality by disjoint pieces
- Kingman's subadditive ergodic theorem
- McDiarmid's inequality
- Asymptotic normality of the maximum likelihood estimator
- Typical set
- Kolmogorov automorphism
- Edgeworth expansion
- Proof that almost sure convergence implies convergence in probability
- Proof of the product formula for the moment-generating function of a sum of independent random variables
- Proof of Hoeffding's inequality via Hoeffding's lemma and the Chernoff bound
- Azuma's inequality
- Erdős–Kac theorem
- Proof of the de Moivre–Laplace theorem from the central limit theorem
- Proof of Chebyshev's inequality
- Monte Carlo integration
- Delta method
- Proof of the Chernoff bound
- Proof that the cumulative distribution function determines the distribution, by the π-λ theorem
- Shannon–Hartley theorem
- Proof of the sum of independent normal random variables by characteristic functions
- Proof of the central limit theorem by characteristic functions
- Berry–Esseen theorem
- Continuity of measure from below and above
- Birkhoff's ergodic theorem
- Hewitt–Savage zero–one law
- Ideal chain
- Bernstein inequalities
- Proof of the second Borel–Cantelli lemma
- Oseledets theorem
- Proof of the addition rule for two events
- Noisy-channel coding theorem
- Proof that convergence in probability implies convergence in distribution
- Flory–Huggins solution theory
- Proof of the multiplication rule
- Kelly criterion
- Pesin theory
- Reptation
- Proof of Kolmogorov's zero–one law
- Proof of the weak law of large numbers by characteristic functions
- Monte Carlo methods in finance
- Proof of the product formula for the characteristic function of a sum of independent random variables
- Bonferroni correction
- Worm-like chain
- Proof of the first Borel–Cantelli lemma
- Proof of the Cauchy–Schwarz inequality for expectations
Further reading
- Web https://en.wikipedia.org/wiki/Probability_axioms, Elementary consequences
About this page
| Tag | FHMWMHSVNS |
|---|---|
| URI | m2://FHMWMHSVNS |
| Kind | proof |
| Writing | W0 stub |
| MSC | 60A05 |
| Proves | Monotonicity of probability |
| Uses | Probability space, Probability measure, Event, Subset, Sigma-algebra, Probability of the complement |
| Changed | 4 November 2026 |
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