MX,Y(s,t) = exp{μxs + μYt + ½(σX2s2 + 2ρσXσYst + σY2t2)} Where X ~ N (μx , σX) and Y ~ N (μY , σY). Also Corr(X,Y) = Cov (X,Y)/{Var(X) . Var(Y)} = ρ
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Using the Taylor series expansion of the exponential function. See related links
Lamdba (like most Greek letters in statistics) usually denotes a parameter of a distribution (usually of Poisson, gamma or exponential distributions). This will specify the entire distribution and allow for numerical analysis of the probability generating, moment generating, probability density/mass, distribution and/or cumulant functions (along with all moments), as and where these are defined.
No, a distribution can have infinitely many moments: the first is the mean, the second variance. Then there are skewness (3), kurtosis (4), hyperskewness (5), hyperflatness (6) and so on.If mk represents the kth moment, thenmk = E[(X - m1)k] where E is the expected value.It is, therefore, perfectly possible for m1 and m2 to be the same but for the distribution to differ at the higher moments.
You mean instantaneous - means happening or completed in a moment, with no delay, immediate
Either an Interval or an Ordinal Scale