How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Change of variables for expectation
Statement
Let be a random element, let be its law, and let or be measurable.
- If , then
- If is integrable, then is integrable with respect to and the same formula holds:
Facts & Assumptions
Given: A random element , its law , and a measurable map as in the Statement.
The law is a probability measure on (Law or distribution of a random element, The law of a random element is a probability measure).
Measurable outer maps preserve measurability under composition (Composition with a Borel measurable outer map preserves measurability).
Every nonnegative measurable function is the increasing limit of nonnegative simple functions, monotone convergence holds, and the nonnegative integral agrees with the simple integral on simple functions (Every nonnegative measurable function is the increasing limit of simple measurable functions, Monotone convergence for the integral, The nonnegative integral agrees with the simple integral on simple functions, The integral of a nonnegative simple function).
Expectation is integration against , and real or complex integrability uses the positive-negative and real-imaginary decompositions (Expectation of a nonnegative or integrable random variable, Integrable real and complex functions, and their integrals).
Measurable functions are closed under the elementary operations used by the integral, and the Lebesgue integral is linear on (Closure properties of measurable functions used by the integral, The Lebesgue integral is linear on ).
Proof
By [L2], the composite is measurable. If is a nonnegative simple function on with the pairwise disjoint, then is a nonnegative simple function on . Using [L1], [L3], and [L4],
Assume now that . By [L3], choose nonnegative simple functions on . Then by step 1.1, so monotone convergence on both spaces and step 1.1 give
Assume is integrable. Then , so step 2.1 applied to gives Hence is -integrable. For real-valued , [L4] and [L5] give with both parts nonnegative, so step 2.1 and linearity yield
If is complex-valued, write with real measurable parts . The inequality and step 3.1 show that and are integrable, so the real-valued case applied to and , followed by complex-linearity from [L5], gives
Step 2.1 proves the nonnegative case, while steps 3.1 and 4.1 prove the integrable real and complex cases.
Depends on
- Law or distribution of a random element
- Expectation of a nonnegative or integrable random variable
- The law of a random element is a probability measure
- Composition with a Borel measurable outer map preserves measurability
- Every nonnegative measurable function is the increasing limit of simple measurable functions
- Monotone convergence for the integral
- The nonnegative integral agrees with the simple integral on simple functions
- The integral of a nonnegative simple function
- Integrable real and complex functions, and their integrals
- The Lebesgue integral is linear on $L^1(\mu)$
- Closure properties of measurable functions used by the integral
Used by
- Brownian paths have infinite total variation Corollary
- Expectation agrees with the published finite weighted sum Corollary
- A light-tailed proposal can give ordinary importance sampling infinite variance Counterexample
- Iid strong law fails at infinite absolute mean Counterexample
- Uniform payoff convergence does not imply gradient convergence Counterexample
- Approximate Bayesian computation and the tolerance-conditioned target Definition
- Characteristic function of a real random variable Definition
- A countable probability space with geometric weights Example
- A degenerate weak limit with persistent expectation Example
- A Gaussian maximum without independence Example
- A smooth integrand satisfies the bound while an integrable singularity does not Example
- An inverse-CDF singularity can be integrable but have infinite variation Example
- Bootstrap and informed proposals on a two-state particle filter Example
- Common random numbers can remove finite-difference noise Example
- Expectation under a measurable transformation Example
- Finite mean does not give a finite-variance Monte Carlo error Example
- Finite-bandwidth Gaussian smoothing Example
- Gaussian KDE boundary bias for a uniform density Example
- Gaussian reparameterization gives two gradient estimators Example
- Normal location score, information, and attainment Example
- Weak convergence of gaussian laws by parameters Example
- ANOVA variance identity for nestedly scrambled point sets Lemma
- Cesaro limit of truncated means Lemma
- Closed form for a Gaussian exponential mixture Lemma
- Doob differences inherit coordinate oscillation widths Lemma
- Gaussian even moments for Brownian increments Lemma
- KL is invariant under a measurable bijection Lemma
- Kullback–Leibler divergence between nondegenerate centered Gaussian laws Lemma
- Location-scale normal laws have their stated mean and variance Lemma
- Oppositely monotone functions of independent coordinates have nonpositive covariance Lemma
- Orthogonal base-b multiresolution and variance decomposition Lemma
- Summability of truncated normalized variances Lemma
- An independent pilot coefficient preserves conditional unbiasedness Theorem
- Antithetic variance and a monotone sufficient condition Theorem
- Conditional density formula Theorem
- Conditional integration through a regular conditional law Theorem
- Conditional-class standardization identifies a causal mean Theorem
- Consistency of the IID Monte Carlo sample variance Theorem
- Consistency of the IID sample mean under a finite first moment Theorem
- Discrete-treatment potential-outcome means are identifiable Theorem
…and 26 more results.
Dependency tree · two levels
32 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Rick Durrett, Probability: Theory and Examples, 5th ed., Section 1.6.3 (standard reference, not scraped)
- S. R. S. Varadhan, Probability Theory, Section 1.4 (standard reference, not scraped)
- J. R. Norris, Probability and Measure, Section 3.3 (standard reference, not scraped)