Conditonal Jensen's Inequality says that for a convex function $\varphi$, a random variable $X$, and a sub-sigma-field $\mathcal{F}$, $E[\varphi(X)\mid \mathcal{F}] \geq \varphi(E[X\mid \mathcal{F}])$. In ordinary Jensen's Inequality, $E[\varphi(X)]\geq \varphi(E[X])$, and we have equality if and only if $X$ is degenerate (i.e., almost surely a constant) or $\varphi$ is linear. I'm wondering if an analogous result holds for the conditional version. Is it the case that $E[\varphi(X)\mid\mathcal{F}]=\varphi(E[X\mid\mathcal{F}])$ if and only if $X \in \mathcal{F}$ or $\varphi$ is linear? (Certainly the "if" is true, but I'm wondering about the "only if.")
2026-04-09 09:23:58.1775726638
Equality in Conditional Jensen's Inequality
6.1k Views Asked by Bumbble Comm https://math.techqa.club/user/bumbble-comm/detail At
1
There are 1 best solutions below
Related Questions in PROBABILITY
- How to prove $\lim_{n \rightarrow\infty} e^{-n}\sum_{k=0}^{n}\frac{n^k}{k!} = \frac{1}{2}$?
- Is this a commonly known paradox?
- What's $P(A_1\cap A_2\cap A_3\cap A_4) $?
- Prove or disprove the following inequality
- Another application of the Central Limit Theorem
- Given is $2$ dimensional random variable $(X,Y)$ with table. Determine the correlation between $X$ and $Y$
- A random point $(a,b)$ is uniformly distributed in a unit square $K=[(u,v):0<u<1,0<v<1]$
- proving Kochen-Stone lemma...
- Solution Check. (Probability)
- Interpreting stationary distribution $P_{\infty}(X,V)$ of a random process
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 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
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?
Method 1: Abbreviate $Y:=E[X|\mathcal F]$. Let $g(x)$ denote the right-hand derivative of $\varphi$ at $x$. Because $\varphi$ is strictly convex, we have $\varphi(x)>g(m)(x-m)+\varphi(m)$ for all $x\not=m$. Thus, $$ \varphi(X)\ge g(Y)(X-Y)+\varphi(Y) $$ with strict inequality off $\{X=Y\}$ (almost surely). Taking conditional expectations in the inequality above we obtain $E[\varphi(X)|\mathcal F]\ge \varphi(Y)$, and $$ \{E[\varphi(X)|\mathcal F]=\varphi(Y)\}\subset\{P[X\not=Y|\mathcal F]=0\} $$ almost surely.
Method 2: Let $\mu(\omega,dx)$ be a regular conditional distribution of $X$ given $\mathcal F$. (Such exists because $X$ is real valued.) That is, for each Borel set $B\subset\Bbb R$, $\omega\mapsto \mu(\omega,B)$ is $\mathcal F$-measurable, for each $\omega\in\Omega$, $B\mapsto \mu(\omega,B)$ is a probability measure on $\Bbb R$, and $\int_{\Bbb R} f(x)\,\mu(\omega,dx)$ is a version of $E[f(X)|\mathcal F](\omega)$ for suitably integrable $f$. Now apply Jensen's inequality (for the strictly convex function $\varphi$) to the probability measure $\mu(\omega,\cdot)$ for each fixed $\omega$. The conclusion is that for $P$-a.e. $\omega\in\Omega$, the equality of $E[\varphi(X)|\mathcal F](\omega)$ and $\varphi(E[X|\mathcal F](\omega))$ forces $\mu(\omega,\cdot)$ to be a unit point mass at $E[X|\mathcal F](\omega)$.