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Characteristic function of a gaussian law
Example
Assume AC. For and , the law has characteristic function including , when the law is .
Facts & Assumptions
Given: The hypotheses and conventions in the example.
The general normal law is the affine pushforward of the standard law. Standard normal and normal laws.
Under AC the standard Gaussian density is a probability density. The standard normal density has total mass one.
The characteristic function is the exponential expectation. Characteristic function of a real random variable.
Affine maps change the transform by scaling frequency and multiplying by a phase. Characteristic functions under affine maps and independent sums.
Dominated limits pass through integrals. Dominated convergence.
Integration by parts applies on compact intervals with integrable derivatives. If are differentiable on with integrable, then .
Compact Riemann integrals agree with Lebesgue integrals under countable choice. A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral.
AC covers Gaussian normalization and the compact integration bridge. The Axiom of Choice.
The real exponential differentiates to itself. The exponential function is smooth and .
Sine and cosine derivatives give the derivative of exp(itx) componentwise. The derivatives of sine and cosine are cosine and minus sine.
The derivative of a composition is the product of derivatives. The chain rule, in one line from Carathéodory: if is differentiable at and is differentiable at , then is differentiable at with .
The integral of an integrable derivative is its primitive increment. The second fundamental theorem: if is differentiable on with and is integrable, then .
A real function with zero derivative on the real interval is constant. A function continuous on an interval whose derivative vanishes at every interior point of is constant on ; consequently two such functions with the same derivative differ by a constant.
Compact substitution permits reflecting a continuous integrand. Substitution: if is differentiable on with integrable and is continuous on an interval containing , then .
Nonnegative density integrals are integrals of products. Integrating against a density agrees with integrating the product.
Nonnegative truncations increasing to a function recover its integral. Monotone convergence for the integral.
A finite first absolute moment justifies differentiating the transform. Moments give derivatives of the characteristic function.
Verification
Let and let have its law on the canonical real probability space. Normalization is supplied by the Gaussian-density lemma. Exponential differentiation and the chain rule give . FTC, reflection substitution and the bridge yield Monotone convergence over positive integer R gives by density integration. Apply the moments lemma at order one: , converting real positive/negative parts of the density integral separately.
On both and the real and imaginary parts of are continuously differentiable. Integration by parts, applied componentwise, gives The boundary term has modulus at most . The left integrand is dominated by the integrable , and the last integral by ; DCT along integer R therefore gives for every real t. No imaginary displacement of an integration contour is involved.
The real and imaginary components of are differentiable. The product and chain rules and step 2.1 give . The zero-derivative theorem applied to each component on makes H constant. Its value at zero is , so . Finally affine scaling gives . If , the random variable is constantly m and its transform is directly , agreeing with the formula. The stated AC is inherited from normalization and the compact integration bridge (including their countable-choice prerequisites).
Depends on
- Characteristic function of a real random variable
- Characteristic functions under affine maps and independent sums
- The standard normal density has total mass one
- Standard normal and normal laws
- Dominated convergence
- If $u,v$ are differentiable on $[a,b]$ with $u',v'$ integrable, then $\int_a^b u v' = u(b)v(b)-u(a)v(a) - \int_a^b u'v$
- A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral
- The Axiom of Choice
- The exponential function is smooth and $(\exp)'=\exp$
- The derivatives of sine and cosine are cosine and minus sine
- The chain rule, in one line from Carathéodory: if $g$ is differentiable at $c$ and $f$ is differentiable at $g(c)$, then $f \circ g$ is differentiable at $c$ with $(f \circ g)'(c) = f'(g(c))\,g'(c)$
- The second fundamental theorem: if $G$ is differentiable on $[a,b]$ with $G' = f$ and $f$ is integrable, then $\int_a^b f = G(b)-G(a)$
- A function continuous on an interval $I$ whose derivative vanishes at every interior point of $I$ is constant on $I$; consequently two such functions with the same derivative differ by a constant
- Substitution: if $\varphi$ is differentiable on $[c,d]$ with $\varphi'$ integrable and $f$ is continuous on an interval containing $\varphi([c,d])$, then $\int_{\varphi(c)}^{\varphi(d)} f = \int_c^d (f\circ\varphi)\,\varphi'$
- Integrating against a density agrees with integrating the product
- Monotone convergence for the integral
- Moments give derivatives of the characteristic function
Used by
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Sources
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)
- Norris, Probability and Measure (standard reference, not scraped)