Let \(f(x)\) and \(g(x)\) be functions that are continuous on \([a,b]\) and differentiable on \((a,b)\). Suppose that \(g'(x) \neq 0\) for all \(x \in (a,b)\). Then, there exists a \(c\in (a,b)\) such that
Proof. For convenience we set \(f(a) = g(a) = 0\) so that \(f(x)\) and \(g(x)\) are defined on \([a, b)\). Assume conditions 1, 2, and 3 hold.
By condition 1, \(f(x)\) and \(g(x)\) are continuous on \([a,b)\). For any \(x\in(a,b)\), \(f(x)\) and \(g(x)\) are continuous on \([a,x]\) and differentiable on \((a,x)\). By condition 2, for all \(t \in (a,x)\), \(g'(t) \neq 0\). Thus, by Cauchy's mean value theorem, there exists a \(c_x \in (a,x)\) such that
\(c_x \to a + 0\) as \(x \to a +0\) and, by condition 3, \(\lim_{x\to a+0}\frac{f'(c_x)}{g'(c_x)}\) exists. Therefore \(\lim_{x\to a+0}\frac{f(x)}{g(x)}\) also exists and
By the definition of the right limit, for any \(\varepsilon > 0\), there exists a \(\delta_1 > 0\) such that \(a < x < a + \delta_1\) implies \(\left|\frac{f'(x)}{g'(x)} - L\right| < \varepsilon\).
Since \(\lim_{x\to a+0}g(x) = \pm\infty\), there exists \(\delta_2 > 0\) such that \(a < x < a + \delta_2\) implies \(|g(x)| > 1\).
Let \(\delta' = \min\{\delta_1, \delta_2\}\) and \(d = a + \delta'\). By Cauchy's mean value theorem, for all \(x \in (a, d)\), there exists a \(c_x \in (x, d)\) such that
Here, \(f(d)\) and \(g(d)\) are finite constants, and \(\lim_{x\to a+0}\frac{f'(x)}{g'(x)}\) converges to a finite value (by condition 3). Hence, by condition 1' (\(\lim_{x\to a + 0}g(x) = \pm\infty\)), \(\lim_{x\to a+0}r(x) = 0\). In other words, for any \(\varepsilon > 0\), there exists a \(\delta_3 > 0\) such that \(a < x < a + \delta_3\) implies \(|r(x)| < \varepsilon\).
Let \(\delta = \min\{\delta', \delta_3\}\). By Eq. (eq:rx), we have
\[\frac{f(x)}{g(x)} - L = \frac{f'(c_x)}{g'(c_x)} - L + r(x).\]
Proof. We prove the case when \(x \to \infty\) and condition 1 holds.
For the open interval \((b, \infty)\), this \(b\) can be replaced with any real number greater than \(b\). Therefore, without losing generality, we may assume \(b > 0\).
Let \(x = \frac{1}{t}\). As \(x \to \infty\), \(t\to +0\). By conditions 1 and 2,
so that, by condition 3, the right limit \(\lim_{t\to +0}\frac{\frac{d}{dt}f\left(\frac{1}{t}\right)}{\frac{d}{dt}g\left(\frac{1}{t}\right)}\) exists. Therefore, by L'H\^opital's rule (1), we have the limit
Let \(\mathbf{X} = (X_1, X_2, \cdots, X_n)^\top\) be a vector of random variables. We say it follows the multivariate normal (Gaussian) distribution if its density is given by \[f(\mathbf{x}) = \frac{1}{\sqrt{(2\pi)^n|\Sigma|}}\exp\left(-\frac{1}{2}(\mathbf{x} - \boldsymbol{\mu})^\top\Sigma^{-1}(\mathbf{x} - \boldsymbol{\mu})\right)\tag{Eq:density}\] where \(\boldsymbol{\mu} = (\mu_1, \mu_2, \cdots, \mu_n)^\top \in \mathbb{R}^n\) is a vector, \(\Sigma\) is a symmetric positive definite \(n\times n\) matrix, and \(\Sigma^{-1}\) and \(|\Sigma|\) are the inverse and determinant of \(\Sigma\), respectively. It turns out that \(\boldsymbol{\mu}\) and \(\Sigma\) are the mean vector and covariance matrix of \(\mathbf{X}\), respectively. But we will not prove that here. In this post, we will show this density (Eq:density) is normalized (of course). That is, we prove that \[\int_{\mathbb{R}^n}f(\mathbf{x})d\mathbf{x} = 1.\] We assume that you already know how to prove the univariate nor...
Let \(\mathbf{X} = (X_1, X_2, \cdots, X_n)^\top \in \mathbb{R}^n\) be a vector of random variables. The covariance matrix \(\Sigma\) of \(\mathbf{X}\) is a square (\(n\times n\)) matrix whose elements are covariances between the components of \(\mathbf{X}\). That is, \[\Sigma_{ij} = \mathrm{Cov}(X_i,X_j)\] where \(\mathrm{Cov}(X_i,X_j)\) is the covariance between \(X_i\) and \(X_j\) , \(i,j = 1, 2, \cdots, n\): \[\mathrm{Cov}(X_i, X_j) = \mathbb{E}[(X_i - \mathbb{E}[X_i])(X_j - \mathbb{E}[X_j])].\] Here, \(\mathbb{E}[\cdot]\) indicates the expectation value of a random variable . Any covariance matrix has the following properties: Symmetric. That is, \[\Sigma = \Sigma^\top.\] Positive semi-definite. That is,\[\forall \mathbf{v} \in \mathbb{R}^n, \mathbf{v}^\top\Sigma\mathbf{v} \geq 0.\] See also : Positive definite matrix (Wolfram MathWorld) The symmetry is obvious from the definition of the covariance matrix. Now, let us prove that the covariance matrix is positive semi-...
We show some basic properties of differentiation, such as linearity, the product rule, the quotient rule, the chain rule, etc. We also introduce higher-order derivatives and differentiability classes. Theorem (Properties of differentiation) Let \(f(x)\) and \(g(x)\) be differentiable functions on an open interval \(I\). (Linearity) \[\frac{d}{dx}[kf(x) + lg(x)] = kf'(x) + lg'(x)\] where \(k, l\) are constants. (Product rule) \[\frac{d}{dx}[f(x)g(x)] = f'(x)g(x) + f(x)g'(x).\] (Quotient rule) \(\frac{f(x)}{g(x)}\) is differentiable on \(\{x \mid g(x) \neq 0, x\in I\}\) and \[\frac{d}{dx}\left[\frac{f(x)}{g(x)}\right] = \frac{f'(x)g(x) - f(x)g'(x)}{[g(x)]^2}.\] Proof . Exercise. Since \(f(x)\) is differentiable at \(x=a\), it is continuous at \(x=a\) so \(f(x) \to f(a)\) as \(x \to a\). For any \(a\in I\), \[\begin{eqnarray*} \frac{f(x)g(x) - f(a)g(a)}{x - a} &=& \frac{f(x)g(x) - f(x)g(a) + f(x)g(a) - f(a)g(a)}{x - a}\\ &=& f(x)\cdot\frac{g(x) ...
Comments
Post a Comment