Let $P,Q$ be two probability measures on $(\Omega,\mathcal{F})$ such that $Q<<P$, let $\mathcal{G}\subset{\mathcal{F}}$ be a sub $\sigma$-algebra. Say $P_{\mathcal{G}},Q_{\mathcal{G}}$ are the restriction of $P$ and $Q$ to $\mathcal{G}$ and denote with $Z=dQ/dP, Z_{\mathcal{G}}=dQ_{\mathcal{G}}/dP_{\mathcal{G}}$ the Radon-Nikodym derivatives. I need to show that, given $X\in{L^1(\Omega,\mathcal{F},Q)}$ and $\forall{G}\in{\mathcal{G}}$ we have: $$\int_{\Omega}1_{{G}}\mathbb{E}_Q[X|\mathcal{G}]dQ=\int_{\Omega}1_{G}\mathbb{E}[X|\mathcal{G}]Z_\mathcal{G}dP.$$ In particular I don't get how we turn an integral in $dQ$ to an integral in $dP$.
2026-02-24 15:23:26.1771946606
Conditional expectation and Radon-Nikodym
165 Views Asked by Bumbble Comm https://math.techqa.club/user/bumbble-comm/detail At
1
There are 1 best solutions below
Related Questions in MEASURE-THEORY
- On sufficient condition for pre-compactness "in measure"(i.e. in Young measure space)
- Absolutely continuous functions are dense in $L^1$
- I can't undestand why $ \{x \in X : f(x) > g(x) \} = \bigcup_{r \in \mathbb{Q}}{\{x\in X : f(x) > r\}\cap\{x\in X:g(x) < r\}} $
- Trace $\sigma$-algebra of a product $\sigma$-algebra is product $\sigma$-algebra of the trace $\sigma$-algebras
- Meaning of a double integral
- Random variables coincide
- Convergence in measure preserves measurability
- Convergence in distribution of a discretized random variable and generated sigma-algebras
- A sequence of absolutely continuous functions whose derivatives converge to $0$ a.e
- $f\in L_{p_1}\cap L_{p_2}$ implies $f\in L_{p}$ for all $p\in (p_1,p_2)$
Related Questions in STOCHASTIC-PROCESSES
- Interpreting stationary distribution $P_{\infty}(X,V)$ of a random process
- Probability being in the same state
- Random variables coincide
- Reference request for a lemma on the expected value of Hermitian polynomials of Gaussian random variables.
- Why does there exists a random variable $x^n(t,\omega')$ such that $x_{k_r}^n$ converges to it
- Compute the covariance of $W_t$ and $B_t=\int_0^t\mathrm{sgn}(W)dW$, for a Brownian motion $W$
- Why has $\sup_{s \in (0,t)} B_s$ the same distribution as $\sup_{s \in (0,t)} B_s-B_t$ for a Brownian motion $(B_t)_{t \geq 0}$?
- What is the name of the operation where a sequence of RV's form the parameters for the subsequent one?
- Markov property vs. transition function
- Variance of the integral of a stochastic process multiplied by a weighting function
Related Questions in CONDITIONAL-PROBABILITY
- Given $X$ Poisson, and $f_{Y}(y\mid X = x)$, find $\mathbb{E}[X\mid Y]$
- Finding the conditional probability given the joint probability density function
- Easy conditional probability problem
- Conditional probability where the conditioning variable is continuous
- probability that the machine has its 3rd malfunction on the 5th day, given that the machine has not had three malfunctions in the first three days.
- Sum of conditional probabilities equals 1?
- Prove or disprove: If $X | U$ is independent of $Y | V$, then $E[XY|U,V] = E[X|U] \cdot E[Y|V]$.
- Conditional probability and binomial distribution
- Intuition behind conditional probabilty: $P(A|B)=P(B\cap A)/P(B)$
- Transition Probabilities in Discrete Time Markov Chain
Related Questions in CONDITIONAL-EXPECTATION
- Expectation involving bivariate standard normal distribution
- Show that $\mathbb{E}[Xg(Y)|Y] = g(Y) \mathbb{E}[X|Y]$
- How to prove that $E_P(\frac{dQ}{dP}|\mathcal{G})$ is not equal to $0$
- Inconsistent calculation for conditional expectation
- Obtaining expression for a conditional expectation
- $E\left(\xi\text{|}\xi\eta\right)$ with $\xi$ and $\eta$ iid random variables on $\left(\Omega, \mathscr{F}, P\right)$
- Martingale conditional expectation
- What is $\mathbb{E}[X\wedge Y|X]$, where $X,Y$ are independent and $\mathrm{Exp}(\lambda)$- distributed?
- $E[X|X>c]$ = $\frac{\phi(c)}{1-\Phi(c)}$ , given X is $N(0,1)$ , how to derive this?
- Simple example dependent variables but under some conditions independent
Related Questions in RADON-NIKODYM
- Prove $E^{\mathbb Q}[Y]=E^{\mathbb P}[XY]$ if $E^{\mathbb P}[X]=\mathbb P(X>0)=1$ and $ \mathbb Q(A)=E^{\mathbb Q}[X1_A] $
- Computing expectation under a change of measure
- Show that the Radon-Nikodym density is $\mathcal{G}$ measurable iff $E_P[X|G] = E_Q[X|G]$
- ergodic measure and absolutely continuous measure
- What is the Radon-Nikodym density $dP^∗ /d P$ of the unique $P^ ∗ ∼ P$ such that the discounted price $S^*_t := S_t /B_t$ is a $P^∗$-martingale
- Application of chain rule, is this correct?
- Radon Nikodym derivative $\frac{\mathrm d(fλ)}{\mathrm d(gλ)}$
- Radon-Nykodym Derivative process-Property of Conditional Expectation
- Finding the Radon-Nikodym Derivative
- The Dual of $L^{\infty}$ and the Radon-Nikodym Theorem
Trending Questions
- Induction on the number of equations
- How to convince a math teacher of this simple and obvious fact?
- Find $E[XY|Y+Z=1 ]$
- Refuting the Anti-Cantor Cranks
- What are imaginary numbers?
- Determine the adjoint of $\tilde Q(x)$ for $\tilde Q(x)u:=(Qu)(x)$ where $Q:U→L^2(Ω,ℝ^d$ is a Hilbert-Schmidt operator and $U$ is a Hilbert space
- Why does this innovative method of subtraction from a third grader always work?
- How do we know that the number $1$ is not equal to the number $-1$?
- What are the Implications of having VΩ as a model for a theory?
- Defining a Galois Field based on primitive element versus polynomial?
- Can't find the relationship between two columns of numbers. Please Help
- Is computer science a branch of mathematics?
- Is there a bijection of $\mathbb{R}^n$ with itself such that the forward map is connected but the inverse is not?
- Identification of a quadrilateral as a trapezoid, rectangle, or square
- Generator of inertia group in function field extension
Popular # Hahtags
second-order-logic
numerical-methods
puzzle
logic
probability
number-theory
winding-number
real-analysis
integration
calculus
complex-analysis
sequences-and-series
proof-writing
set-theory
functions
homotopy-theory
elementary-number-theory
ordinary-differential-equations
circles
derivatives
game-theory
definite-integrals
elementary-set-theory
limits
multivariable-calculus
geometry
algebraic-number-theory
proof-verification
partial-derivative
algebra-precalculus
Popular Questions
- What is the integral of 1/x?
- How many squares actually ARE in this picture? Is this a trick question with no right answer?
- Is a matrix multiplied with its transpose something special?
- What is the difference between independent and mutually exclusive events?
- Visually stunning math concepts which are easy to explain
- taylor series of $\ln(1+x)$?
- How to tell if a set of vectors spans a space?
- Calculus question taking derivative to find horizontal tangent line
- How to determine if a function is one-to-one?
- Determine if vectors are linearly independent
- What does it mean to have a determinant equal to zero?
- Is this Batman equation for real?
- How to find perpendicular vector to another vector?
- How to find mean and median from histogram
- How many sides does a circle have?
The important fact here, is that for any $f\in L^1(\Omega,\mathcal F,P)$, we know that $\int_\Omega fdQ=\int_\Omega fZdP$ (this is true if we replace $\mathcal F$ with $\mathcal G$ and the restricted measures replacing the original). You can prove this by noting that this is true for simple functions simply by the definition of the Radon-Nikodym derivative, and then using the usual approximation and limiting argument. We also know that $\int_G\mathbb E_Q[X|\mathcal G]dQ=\int_G\mathbb E_Q[X|\mathcal G]dQ_{\mathcal G}$ (see here). Thus, combining these and using the definition of conditional expectation: $$\int_G\mathbb E_Q[X|\mathcal G]dQ=\int_G\mathbb E_Q[X|\mathcal G]dQ_{\mathcal G}=\int_\Omega\mathbb 1_G\mathbb E_Q[X|\mathcal G]Z_{\mathcal G}dP_{\mathcal G}=\int_\Omega\mathbb 1_G\mathbb E_Q[X|\mathcal G]Z_{\mathcal G}dP.$$ Now, finally, using standard properties of conditional expectation and recalling that $Z_\mathcal G$ is $\mathcal G$ measurable, we have $$\int_\Omega\mathbb 1_G\mathbb E_Q[X|\mathcal G]Z_{\mathcal G}dP_{\mathcal G}=\int_\Omega\mathbb 1_G\mathbb E_Q[X|\mathcal G]\mathbb E_P[Z_{\mathcal G}|\mathcal G]dP_{\mathcal G}=\int_\Omega\mathbb 1_G\mathbb E_P[XZ_{\mathcal G}|\mathcal G]dP_{\mathcal G}=\int_\Omega\mathbb 1_G\mathbb E_P[X|\mathcal G]Z_{\mathcal G}dP_{\mathcal G}$$ where the second last equality follows from the so called "abstract Bayes formula". Thus, because $\mathbb 1_G\mathbb E_P[X|\mathcal G]Z_{\mathcal G}$ is $\mathcal G$ measurable, we conclude: $$\int_G\mathbb E_Q[X|\mathcal G]dQ=\int_\Omega\mathbb 1_G\mathbb E_P[X|\mathcal G]Z_{\mathcal G}dP$$.