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EE(g(X)jY)=E(g(X)) Proof Set Z = g(X) Statement (i) of Theorem 1 applies to any two rv's Hence, applying it to Z and Y we obtain EE(ZjY)= E(Z) which is the same as EE(g(X)jY)= E(g(X)) 2 This property may seem to be more general statement than (i) in Theorem 1 The proof above shows that in fact these are equivalent statements 3E g(X) ≥ g( EX ) Source Wolfram MathWorld a) Proof Let g(X) be −√$, for all positive real numbers g(x) is a convex function as seen by the graph to the right Since g(X) satisfies the requirements for Jensen's inequality, we can use it to directly prove the inequalityLinks with this icon indicate that you are leaving the CDC website The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a nonfederal website Linking to a nonfederal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website
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A plot of the Qfunction In statistics, the Qfunction is the tail distribution function of the standard normal distribution In other words, Q ( x ) {\displaystyle Q (x)} is the probability that a normal (Gaussian) random variable will obtain a value larger than x {\displaystyle x} standard deviationsA N X ́A e r Q ̎ Ӌ@ g f B O J h Q ̃T v C Ȃǂ̊J A A ̔ J ł BHere we will look at solving a special class of Differential Equations called First Order Linear Differential Equations First Order They are "First Order" when there is only dy dx, not d 2 y dx 2 or d 3 y dx 3 etc Linear A first order differential equation is linear when it can be made to look like this dy dx P(x)y = Q(x) Where P(x) and Q(x) are functions of x To solve it there is a
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