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In general, if X is a real-valued random variable defined on a probability space (Ω, Σ, P), then the expected value of X, denoted by E[X], is defined as the Lebesgue integral [18] [] =. Despite the newly abstract situation, this definition is extremely similar in nature to the very simplest definition of expected values, given above, as ...
In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated with respect to the conditional probability distribution. 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 ...
Note that the conditional expected value is a random variable in its own right, whose value depends on the value of . Notice that the conditional expected value of given the event = is a function of (this is where adherence to the conventional and rigidly case-sensitive notation of probability theory becomes important!).
The proposition in probability theory known as the law of total expectation, [1] the law of iterated expectations [2] (LIE), Adam's law, [3] the tower rule, [4] and the smoothing theorem, [5] among other names, states that if is a random variable whose expected value is defined, and is any random variable on the same probability space, then
The expected value of X is (+ + + + +) / = / Therefore, the variance of X is ... the population variance of a finite population of size N with values x i is given by ...
If X = X * then the random variable X is called "real". An expectation E on an algebra A of random variables is a normalized, positive linear functional. What this means is that E[k] = k where k is a constant; E[X * X] ≥ 0 for all random variables X; E[X + Y] = E[X] + E[Y] for all random variables X and Y; and; E[kX] = kE[X] if k is a constant.
Gen X is expected to inherit $14 trillion by 2034 and $39 trillion by 2048. Put another way, of the $2.5 trillion being passed down every year, about $1 trillion is going to Gen Xers.
This shows that the expected value of g(X) is encoded entirely by the function g and the density f of X. [ 6 ] The assumption that g is differentiable with nonvanishing derivative, which is necessary for applying the usual change-of-variables formula, excludes many typical cases, such as g ( x ) = x 2 .