linearity of conditional expectation

When dealing with a drought or a bushfire, is a million tons of water overkill? we can consider ``the expectation of the conditional expectation ,'' and compute it as follows. what we need are ways to express, interpret, and compute conditional probabilities of events and conditional expectations of random variables, given -algebras. I believe I was misdiagnosed with ADHD when I was a small child. Example 5.38 Recall Example 4.42. Conditional expectation, given a random vector, plays a fundamental role in much of modern probability theory. /Length 3941 To estimate \(\textrm{E}(Y)\) and \(\textrm{E}(XY)\) by conditioning on \(X\) and using the law of total expectation, we first sort and group the rectangles according to the value of their base \(X\). However, if we take into consideration that the main . If g(x) h(x) for all x R, then E[g(X)] E[h(X)]. Theorem 5.4 (Taking out what is known (TOWIK)) \[ Therefore \(\textrm{E}(g(X)Y|X=x) = \textrm{E}(g(x)Y|X=x)= g(x)\textrm{E}(Y|X=x)\), where \(g(x)\) pops out of the expected value since it is just a number. For a given value \(x\) of \(X\), \(\textrm{E}(Y|X=x)\) is a number. . Free essays on the reformation. Out of the framework of Linear Theory, a signicant role plays the independence concept and conditional expectation. Guitar for a patient with a spinal injury, Defining inertial and non-inertial reference frames, How do I add row numbers by field in QGIS, scifi dystopian movie possibly horror elements as well from the 70s-80s the twist is that main villian and the protagonist are brothers. A rectangle example like the one in Example 5.48 illustrates the ideas behind the law of total expectation and taking out what is known. Expectation Recall that the expected value of a real valued random variable is dened: E[ X] = x p( = x) . \end{align*}\], \(\textrm{E}(X)= (-1)(0.40) + (0)(0.20)+(1)(0.40) = 0\), \[\begin{align*} The conditional expectation of X given event subspace E is denoted E[XjE] and is a random variable Z =E[XjE] where . We show how to think about a conditional expectation E(Y|X) of one r.v. We now have the average height and average area of the rectangles in each group. G, then the result is a consequence of the denition of conditional expectation and linearity. \textrm{E}(a_1Y_1+\cdots+a_n Y_n|X=x) & = a_1\textrm{E}(Y_1|X=x)+\cdots+a_n\textrm{E}(Y_n|X=x)\\ As long as no one wins, you keep switching off who points and who looks. The following are properties of conditional expectation. & = \sum_y y \sum_x p_{X, Y}(x, y) & & \text{interchange sums}\\ To approximate \(\textrm{E}(Y|X = x)\), simulate many \((X, Y)\) pairs, discard the pairs for which \(X\neq x\), and average the \(Y\) values for the pairs that remain. \[ >> Here we go: \], \(\textrm{E}(E(X|Y))=\textrm{E}(1.5Y+0.5)=1.5\textrm{E}(Y) + 0.5=1.5(3)+0.5\), \(\textrm{E}(Y) = \int_1^4 y (2/9)(y-1)dy = 3\), \[ The values of \(\textrm{E}(Y|R)\) would be given by \(30 + 0.7(R - 30)\), a function of \(R\). Definition 5.8 The conditional expected value of \(Y\) given \(X\) is the random variable, denoted \(\textrm{E}(Y|X)\), which takes value \(\textrm{E}(Y|X=x)\) on the occurrence of the event \(\{X=x\}\). Stack Overflow for Teams is moving to its own domain! Published on Monday, February 26, 2018. . If \(X\) and \(Y\) are independent, then the conditional distribution of \(Y\) is the same for all values of \(X\), and so the mean of \(Y\) is the same for all values of \(X\). 1 What you want to show is that the mapping $X\mapsto \mathbb{E}[X|Y]$is linear. Intuitively, when we condition on \(X\) we treat it as though its value is known, so it behaves like a non-random constant. Need an upper bound for a simple expectation involving Rademacher random variables. The random variable \(Z=\ell(X)\) takes values 1, 2, 8/3, 3.5, 11/3, and 4. \], \(\textrm{E}(Y|X = x) = \frac{0.5x + x - 1}{2} = 0.75x - 0.5\), \(\textrm{E}(Y|X = x) = \frac{0.5x + 4}{2} = 0.25x +2\), \[ 2 Moments and Conditional Expectation Using expectation, we can dene the moments and other special functions of a random variable. Conditional expected value, whether viewed as a number \(\textrm{E}(Y|X=x)\) or a random variable \(\textrm{E}(Y|X)\), possesses properties analogous to those of (unconditional) expected value. How can I draw this figure in LaTeX with equations? \[\begin{align*} \textrm{E}(Y | X = x) = 0.25x + 0.5\min(4, x-1). Morespecically: AssumeY E withE = {y . For all constants Cl and C2, we have lEn[clX + $$\forall A\in\sigma(Y), \mathbb{E}[X\mathbf{1}_A]=\mathbb{E}\left[\mathbb{E}[X|Y]\mathbf{1}_A\right]$$. The best answers are voted up and rise to the top, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company, $E((\sum_{i=1}^{n} X_{i})|Y) = \sum_{i=1}^{n} E (X_{i}|Y)$, $$\forall A\in\sigma(Y), \mathbb{E}[X\mathbf{1}_A]=\mathbb{E}\left[\mathbb{E}[X|Y]\mathbf{1}_A\right]$$, $Z=\lambda \mathbb{E}[X|Y]+\mu\mathbb{E}[X'|Y]$, Linearity of conditional expectation (proof for n joint random variables), Mobile app infrastructure being decommissioned, Conditional Expectation of Random Sum of Random Variables, Conditional Expectation of A respect to A+B. For example, \(\textrm{E}(XY|X)=X\textrm{E}(Y|X)\) is the conditional, random variable analog of the unconditional, numerical relationship \(\textrm{E}(cY) = c\textrm{E}(Y)\) where \(c\) is a constant. Consider Figure 5.6 which represents the situation in Example 5.39. In other words, it is the expected value of one variable given the value (s) of one or more other variables. Example 5.39 Recall Example 2.63. & = \textrm{P}(1.5Y + 0.5 \le w)\\ However, remember that a transformation generally changes the shape of a distribution, so the distribution of \(\textrm{E}(Y|X)\) will usually have a different shape than that of \(X\). &= \sum_{i=1}^{k+1} a_i~E(X_i \mid Y=y) \\ If you had two random variables X and Y, then to compute the expectation of their sum 72 CHAPTER 7. Example 5.45 Flip a fair coin repeatedly. For \(2> Is upper incomplete gamma function convex? Conditioningar.vbyadiscreter.v Example4:WheneverX andY arediscreterandomvariables,ComputationofE[X|Y] canbehandledasinexample3. Compute and interpret the expected number of rounds in a game. The base \(X\) is a random variable with a Uniform(0, 1) distribution. could you launch a spacecraft with turbines? Properties of Conditional Expectation: Let X 2L2(,F,P) and let G be a algebra contained in F. Then (0) Linearity: E(aX1 bX2 jG) aE(X1 jG)bE(X2 jG). What to throw money at when trying to level up your biking from an older, generic bicycle? %PDF-1.4 provides a way of computing an expected value by breaking down a problem into various cases, computing the conditional expected value given each case, and then computing the overall expected value as a probability-weighted average of these case-by-case conditional expected values. 13. rev2022.11.10.43023. If the random variable can take on only a finite number of values, the "conditions" are that the variable can only take on a subset of those values. For example, there would be more days where Regina arrives 40 minutes after noon than where she arrives 15 minutes after noon, so guesses of 37 minutes after noon (\(\textrm{E}(Y|R=40)=37\)) would occur more frequently than guesses of 19.5 minutes after noon (\(\textrm{E}(Y|R=15)=19.5\)). \end{align*}\], \[ Thus, by Denition 1, to show that this linear combination is the conditional expectation E(aU bV jY), it sufces to show that it satises equation (6), that is, that for every bounded nonnegative function g(Y), (HT is 2 flips, HHT is 3 flips, THT is 3 flips, HHHT is 4 flips, etc), What is the expected value of the number of flips until you see H followed immediately by H? But not all groups have the same number of rectangles. CONDITIONAL EXPECTATION AND MARTINGALES and we wish to minimize this over all possible Grandom variables Z. Use the table above. Various types of "conditioning" characterize some of the more important random sequences and processes. Otherwise, you switch roles and the game continues to the next round now your friend points in a direction and you try to look away. \textrm{E}(Y) = \textrm{E}(\textrm{E}(Y|X)) = \textrm{E}(X/2) = \textrm{E}(X)/2 = (0.5)/2 = 0.25 If a distribution changes, its summary characteristics like expected value and variance can change too. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. When making ranged spell attacks with a bow (The Ranger) do you use you dexterity or wisdom Mod? It takes longer on average to see HH than HT. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Asking for help, clarification, or responding to other answers. Do conductor fill and continual usage wire ampacity derate stack? \], \(\textrm{E}(XY|X=0.5)=\textrm{E}(0.5Y|X=0.5) = 0.5\textrm{E}(Y|X=0.5) = 0.5(0.25)=0.125\), \(\textrm{E}(XY|X=0.2)=\textrm{E}(0.2Y|X=0.2) = 0.2\textrm{E}(Y|X=0.2) = 0.2(0.1) = 0.02\), \(\textrm{E}(XY|X=x)=\textrm{E}(xY|X=x) = x\textrm{E}(Y|X=x)\), \(\textrm{E}(XY|X=x)=x\textrm{E}(Y|X=x)\), \(\textrm{E}(XY|X=x)=x\textrm{E}(Y|X=x)=x(x/2)=x^2/2\), \[ stream Use MathJax to format equations. general linear regression model assumptionslife celebration memorial powerpoint template. Given a value \(x\) of \(X\), the conditional expected value \(\textrm{E}(Y|X=x)\) is a number. \textrm{E}(X|Y=0) & = (-1)(0) + (0)(0)+(1)(1) = 1\\ This is true for any value \(y\) of \(Y\). xZIsWfJ 9MR$dr)qogr- Z#dX_}k,DUywr~y"+)laNlifo|sv~L9g2{\Vyy3}4o; ?>oo]1&[a9+|("^"'w-@?3T*_/y&7cjf*EL)hT.wY}|kztX%l#ye}Yh,nv5-Gmo@"96]m#7,#m+M"m6k"W%`6|Ce`C0 JB?kkwhl+KSwzkv.]P@E c]ekA5?@+7Fq#M&G2@DEv It only takes a minute to sign up. The first line above is an equality involving numbers; the second line is an equality involving random variables (i.e., functions). We see that \(\textrm{E}(\textrm{E}(X|Y)) = 5 = \textrm{E}(X)\). Proof that linearity of expectation holds for countably infinite sum of random variables $(X_n)$ given $\sum_{i=1}^{\infty}E[|X_i|]$ converges? We are interested in \(\textrm{E}(XY)\) the expected value of the area of the rectangle. \], \(\textrm{E}(\textrm{E}(X|Y)) = 5 = \textrm{E}(X)\), \[ \(\epsilon=0.005\) if rounding to two decimal places). &+a_{k+1}\int_{-\infty}^{\infty}x_{k+1}~dx_{k+1}\underbrace{\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}}_{k~ \text{integrals}}f_{X_1,,X_k,X_{k+1}|Y}(x_1,,x_{k+1}|y)~dx_1dx_{k}\\ MathJax reference. \textrm{P}(\textrm{E}(X|Y) \le 5) = \textrm{P}(1.5Y + 0.5 \le 5) = \textrm{P}(Y \le 3) = \int_1^3 (2/9)(y-1)dy = 4/9 The table below defines the function \(\ell\). lXWPU~Oc7XX#O=*%j.8^gd{(-njTPB[}9>F0|Hp Linearity of conditional expectation: I want to prove E( n i = 1aiXi | Y = y) = n i = 1ai E(Xi | Y = y) where Xi, Y are random variables and ai R. I tried using induction (the usual, assume it's true for n=k, and prove it for n=k+1), so I get, in the continuous case, E(k + 1 i = 1aiXi | Y = y) = E( k i = 1aiXi + ak + 1Xk + 1 | Y = y) = . The conditional expected value \(\textrm{E}(Y | X=x)\) is the long run average value of \(Y\) over only those outcomes for which \(X=x\). The solution is given. We saw in Example 3.5 that the probability that the player who starts as the pointer wins the game is 4/7 = 0.571. 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linearity of conditional expectation