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Sity function and the distribution function of X, respectively Note that F x (x) =P(X ≤x) and fx(x) =F(x) When X =ψ(Y), we want to obtain the probability density function of YLet f y(y) and F y(y) be the probability density function and the distribution function of Y, respectively Inthecaseofψ(X) >0,thedistributionfunctionofY, Fy(y), is rewritten as followsD \ T o 2 !ª ¿ Ú &« z Ð Ö á5Â È ù Ò Ú Ç i°5 D \ T o 2 ã Ò á 1 â!ª ¿ Î ã ý } æ (» â Ô Ñ9Þ9æ @ 6 ;Y I N n E X ́u K f Z ~ i v \ ŊJ Â Ă ܂ B K f ̖X A Ȃǂ̐ A q l ̃K f ̂ k Ȃǂ ܂ B



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Proof lnexy = xy = lnex lney = ln(ex ·ey) Since lnx is onetoone, then exy = ex ·ey 1 = e0 = ex(−x) = ex ·e−x ⇒ e−x = 1 ex ex−y = ex(−y) = ex ·e−y = ex · 1 ey ex ey • For r = m ∈ N, emx = e z }m { x···x = z }m { ex ···ex = (ex)m • For r = 1 n, n ∈ N and n 6= 0, ex = e n n x = e 1 nx n ⇒ e n x = (ex) 1 • For r rational, let r = m n, m, n ∈ NWhere b is a positive real number, and the argument x occurs as an exponent For real numbers c and d, a function of the form () = is also an exponential function, since it can be rewritten as = () As functions of a real variable, exponential functions are uniquely characterized by the fact that the growth rate of such a function (that is, its derivative) is directly proportional to the19 N4 26 Vol348 q X ^ ~ s Ϗǂ āH ̌ N ̂ ߂ɁA R u ` i g L m R j A X Ȃǂ̔ y H i A R s L x ȃg } g A ǎ ̎ ܂ރA { J h ێ悷 ȂǁA N ɋC t Ă l ͑ Ǝv ܂ B Ȃ̂ɁA C C A 畆 ɂ ݂⎼ ł A @ o Ėڂ Ȃǂ̃A M Ǐ A ɂɔY ܂ ꂽ A S o N o N Ă ƁA u Ȃ H v Ǝv Ƃ͂ ܂ H @ t ɑ k Ă A Ǐ ̌ Ȃ 悤 Ȃ A q X ^ ~ s Ϗǂ l 邩 ܂ B



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