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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
Dependency tree · two levels
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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)