Probability theory and stochastic processes Proof
Proof of the probability of the complement
Builds on Probability spaceProbability measureEventComplementSigma-algebraThe reals are Archimedean
Proof
- Let be a probability space and let be an event.
- The family is a σ-algebra. So the complement is in , and so is .
- We first show . Let , a number in .
- The sets for are pairwise disjoint and their union is . By the countable additivity of the probability measure , we get .
- Suppose that . By the Archimedean property, there is with . Then the partial sums of exceed from the index on, so the series does not converge to .
- This contradicts step 4. So , that is, .
- Now let , and for . These sets are pairwise disjoint, because and meets no set.
- Their union is .
- Countable additivity and step 6 give .
- Since , we get . Hence .
Idea
An event and its complement split the sample space into two disjoint pieces. Additivity turns this split into a sum of probabilities, and the whole space has probability . The only care needed is to get finite additivity from countable additivity, which uses .
Reading path
To understand this page from the ground up, read these 50 pages in order. The foundations come first. The path takes the proofs Proof of the approximation property of the supremum, 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
Where this leads
132 pages build on this one.
- Prior probability
- Chernoff bound
- Chapman–Kolmogorov equation
- Chebyshev's inequality
- Characteristic function of a sum of independent random variables
- Law of total variance
- Cauchy–Schwarz inequality for expectations
- Monotonicity of probability
- Law of total expectation
- 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
- Base rate fallacy
- Law of total probability
- Boole's inequality
- Convergence in probability implies convergence in distribution
- Central limit theorem
- The cumulative distribution function determines the distribution
- Bayesian inference
- Posterior probability
- Coupon collector's problem
- Bayes' theorem
- Birthday problem
- Second Borel–Cantelli lemma
- Sum of independent normal random variables
- Monty Hall problem
- Probability of the complement
- Cumulant generating function
- Limit of the cumulative distribution function at infinity
- Almost sure convergence implies convergence in probability
- First Borel–Cantelli lemma
- Law of total probability for an event and its complement
- Gambler's ruin
- 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
- Absorbing Markov chain
- Sample average approximation
- Bernstein–von Mises theorem
- Cramér–Rao bound
- Efficiency of an estimator
- Bayesian search theory
- 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
- Empirical Bayes method
- Proof of the law of total variance
- Asymptotic equipartition property
- Bayes factor
- Quantum Fisher information
- Lindley's paradox
- Source–channel separation theorem
- Proof of the limit of the cumulative distribution function at infinity
- Bayes estimator
- Proof of Boole's inequality by disjoint pieces
- Kingman's subadditive ergodic theorem
- McDiarmid's inequality
- Asymptotic normality of the maximum likelihood estimator
- Partially observable Markov decision process
- Typical set
- Kolmogorov automorphism
- Edgeworth expansion
- Proof that almost sure convergence implies convergence in probability
- Continuous-time Markov chain
- Data assimilation
- 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
- Aumann's agreement theorem
- Monte Carlo integration
- Delta method
- Proof of the Chernoff bound
- Belief propagation decoding
- Proof that the cumulative distribution function determines the distribution, by the π-λ theorem
- Proof of the law of total probability for an event and its complement
- Shannon–Hartley theorem
- Signaling game
- Proof of the monotonicity of probability
- Bayes risk
- 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
- Bayesian game
- Birkhoff's ergodic theorem
- Hewitt–Savage zero–one law
- Credible interval
- Ideal chain
- BCJR algorithm
- Bernstein inequalities
- Probabilistic seismic hazard analysis
- Proof of the second Borel–Cantelli lemma
- Oseledets theorem
- Jeffreys prior
- Proof of the addition rule for two events
- Noisy-channel coding theorem
- Bayesian hierarchical model
- Proof of the law of total probability
- Proof that convergence in probability implies convergence in distribution
- Flory–Huggins solution theory
- Birthday attack
- Conjugate prior
- Proof of the multiplication rule
- Kelly criterion
- Shrinkage estimator
- Fixation probability
- Pesin theory
- Reptation
- Proof of Kolmogorov's zero–one law
- Particle filter
- No-trade theorem
- 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 | 7S3AK5DHEP |
|---|---|
| URI | m2://7S3AK5DHEP |
| Kind | proof |
| Writing | W0 stub |
| MSC | 60A05 |
| Proves | Probability of the complement |
| Uses | Probability space, Probability measure, Event, Complement, Sigma-algebra, The reals are Archimedean |
| Changed | 4 November 2026 |
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