Definition

For \(0<p<\infty\), \(L^p(\Omega, \mathcal{F}, \mu)\) is defined to be the space of all measurable, extended real-valued functions \(f\) for which \(|f|^p\) is integrable, i.e., \[ L^p(\Omega,\mathcal{F},\mu)= \left\{f:\int |f|^p\mbox{ } d\mu<\infty,\mbox{ }f\mbox{ measurable} \right\}. \] When the measure space is understood, \(L^p(\Omega, \mathcal{F}, \mu)\) is often abbreviated to \(L^p\). For \(1\le p<\infty\), the \(p-\)norm of \(f\in L^p\) is given by \[ ||f||_p=\left[ \int |f|^p\mbox{ } d\mu \right]^{1/p}. \] For \(p=\infty\), the infinity norm of \(f\) is defined as \[ ||f||_\infty=\inf\left\{\alpha:\mu(\{\omega:|f(\omega)|>\alpha\})=0\right\}, \] where \(\inf\phi:=\infty\). If \(||f||_\infty<\infty\), then \(f\) is said to be essentially bounded and \(L^\infty=L^\infty(\Omega, \mathcal{F},\mu)\) is defined to be the space of all essentially bounded functions \(f\).




Lemma

If \(0\le a,b<\infty\) and \(0\le \lambda\le 1\), then \(a^\lambda b^{1-\lambda}\le \lambda a + (1-\lambda)b\), where we take \(0^0=1\).



Lemma

If \(c,d\ge 0\), \(1\le p,q<\infty\), and \(\frac{1}{p}+\frac{1}{q}=1\), then \[ cd\le \frac{c^p}{p}+\frac{d^q}{q}. \]



Theorem(Holder’s Inequality)

If \(f\in L^p\) and \(g\in L^q\), where \(1\le p,q\le \infty\) and \(\frac{1}{p}+\frac{1}{q}=1\), then \(fg\in L^1\) and \[ ||fg||_1\le ||f||_p||g||_q \]

\[\begin{eqnarray*} cd\le \frac{c^p}{p}+\frac{d^q}{q} &\implies& \frac{|f(\omega)g(\omega)| }{||f||_p ||g||_q}\le \frac{|f(\omega)|^p}{p||f||_p^p} +\frac{|g(\omega)|^q}{q||g||_q^q}\mbox{ }\mbox{ }\mbox{ }\forall \omega\in \Omega\\ &\stackrel{\text{Monotonicity}}\implies& \int \frac{|f(\omega)g(\omega)| }{||f||_p ||g||_q}d\mu\le\int\frac{|f(\omega)|^p}{p||f||_p^p}d\mu +\int\frac{|g(\omega)|^q}{q||g||_q^q}d\mu\\ &\implies& \frac{||fg||_1}{||f||_p ||g||_q}\le \frac{1}{p}+\frac{1}{q} =1 \\ &\implies& ||fg||_1\le ||f||_p ||g||_q. \end{eqnarray*}\]



Corollary (Cauchy-Schwarz Inequality)

If \(f,g\in L^2\), then \(fg\in L^1\) and \(||fg||_1\le ||f||_2||g||_2\).



Lemma

For any \(a,b\ge 0\) and \(0<p<\infty\), \[ (a+b)^p\le 2^p(a^p+b^p). \]



Theorem

If \(f,g\in L^p\), \(0<p\le\infty\), then \(f+g\in L^p\).




Theorem(Minkowski’s Inequality)

If \(f,g\in L^p\), \(1\le p\le \infty\), then \[ ||f+g||_p\le ||f||_p+||g||_p. \]



Proposition

If \(c\in\mathbb{R}\) and \(f\in L^p\) for some \(1\le p\le \infty\), then \(cf\in L^p\) and \[ ||cf||_p=|c|||f||_p. \]



Theorem도 증명도 매우 중요하다.

Theorem (Markov’s Inequality)

For any \(\alpha>0\) \[ \mu(\{\omega:|f(\omega)|\ge \alpha \})\le \frac{1}{\alpha}\int|f|\mbox{ }d\mu. \]

나중에 증명에 사용되니 참고하자.



Theorem (Lyapounov’s Inequality)

If \(0<\alpha\le\beta\) and \(X\in L^{\beta}\), then \(X\in L^\alpha\) and \(\left\{E(|X|^\alpha) \right\}^{1/\alpha}\le \left\{E(|X|^\beta) \right\}^{1/\beta}\).



매우 자주 쓰인다.

Theorem (Jensen’s Inequality)

If \(f\) is convex on an interval \(I\subset \mathbb{R}\) containing the range of the random variable \(X\), and if \(X\) is integrable, then \[ f(E(X))\le E(f(X)). \]



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