# expected value of a probability distribution

∫ {\displaystyle (\Delta A)^{2}=\langle {\hat {A}}^{2}\rangle -\langle {\hat {A}}\rangle ^{2}} A This value is calculated by multiplying possible results by the likelihood of every result will appear and then take gross of all these values. ] ≥ {\displaystyle c_{i}} X ∫ ) : X ψ This makes sense with our intuition as one-half of 3 is 1.5. ⁡ ( ( {\displaystyle X\geq 0} 1 The expected value of X is given by the formula: E(X) = x1p1 + x2p2 + x3p3 + . = Y E ω {\displaystyle \operatorname {E} (X)=\operatorname {E} (X^{+})-\operatorname {E} (X^{-})} X i i {\displaystyle \operatorname {E} [X_{n}]\to \operatorname {E} [X]} max with a countable number of outcomes, set This is mainly used in statistics and probability analysis. { ) E , denoted ) X ≥ = k … this advantage in the theory of chance is the product of the sum hoped for by the probability of obtaining it; it is the partial sum which ought to result when we do not wish to run the risks of the event in supposing that the division is made proportional to the probabilities. = This principle seemed to have come naturally to both of them. [ To empirically estimate the expected value of a random variable, one repeatedly measures observations of the variable and computes the arithmetic mean of the results. with elements A ), then the weighted average turns into the simple average. + X ( is no longer guaranteed to be well defined at all. is defined as. ⁡ p ( ) ≥ ) {\displaystyle \displaystyle X^{-}(\omega )=\int \limits _{-X^{-}(\omega )}^{0}dx=\int \limits _{-\infty }^{0}{\mathbf {1} }{\{x\mid X^{-}(\omega )\geq -x\}}\,dx=\int \limits _{-\infty }^{0}{\mathbf {1} }{\{(\omega ,x)\mid X(\omega )\leq x\}}\,dx. This formula can also easily be adjusted for the continuous case. ∫ ) The expectation of a random variable plays an important role in a variety of contexts. xn, and respective probabilities of p1, p2, p3, . ∞ {\displaystyle \mathop {\hbox{E}} } To see this, let More than a hundred years later, in 1814, Pierre-Simon Laplace published his tract "Théorie analytique des probabilités", where the concept of expected value was defined explicitly:[8]. 2 2 1 But, {\displaystyle +\infty .} ω . } In general, if n ] x [ , I have had therefore to examine and go deeply for myself into this matter by beginning with the elements, and it is impossible for me for this reason to affirm that I have even started from the same principle. p n f , k {\displaystyle X} . X g X = Ω = E E ] [4] The expected value of a general random variable involves integration in the sense of Lebesgue. ) P {\displaystyle \sigma } x = n < E ∞ , ] If the expected value exists, this procedure estimates the true expected value in an unbiased manner and has the property of minimizing the sum of the squares of the residuals (the sum of the squared differences between the observations and the estimate). If some of the probabilities and hence } . x − Three years later, in 1657, a Dutch mathematician Christiaan Huygens, who had just visited Paris, published a treatise (see Huygens (1657)) "De ratiociniis in ludo aleæ" on probability theory. A ) x j Recalling that , then the expected value is defined as the Lebesgue integral. 0 ⁡ X is defined as the arithmetic mean of the terms Note that the letters "a.s." stand for "almost surely"—a central property of the Lebesgue integral. x U i ( ^ to denote expected value goes back to W. A. Whitworth in 1901. → This formula makes an interesting appearance in the St. Petersburg Paradox. ⁡ ) ( ⁡ In German, p c ∣ 2 X For example, if you were rolling a die, it can only have the set of numbers {1,2,3,4,5,6}.