Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
L'Hôpital's rule
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We have been using the following formula without proof so far:
\[\lim_{x\to 0}\frac{\sin x}{x} = 1.\]
In this example, both \(\sin x\) and \(x\) converges to 0 as \(x \to 0\) so we have something like \(\frac{0}{0}\). In general, if \(\lim_{x\to a}f(x) = \lim_{x\to a}g(x) = 0\) or \(\lim_{x\to a}f(x) = \lim_{x\to a}g(x) = \pm\infty\), the limit of the form \(\lim_{x\to a}\frac{f(x)}{g(x)}\) is called an indeterminate form. L'Hôpital's rule provides a convenient way to calculate such limits (The proof will be given in another post).
Also the right limits \(\lim_{x \to a+0}\) may be replaced with the left limits \(\lim_{x\to a-0}\).
Corollary (L'Hôpital's rule (2))
Let \(f(x)\) and \(g(x)\) be differentiable functions on an open interval \(I\) that contains \(a\). Suppose \(f(x)\) and \(g(x)\) satisfy the following conditions.
\[\lim_{x \to a}f(x) = \lim_{x\to a}g(x) = 0.\]
For all \(x \in I\setminus\{a\}\), \(g'(x) \neq 0\).
The limit \(\lim_{x\to a}\frac{f'(x)}{g'(x)}\) exists.
Then, the limit \(\lim_{x\to a}\frac{f(x)}{g(x)}\) exists and
Remark. The same corollary holds if \(f(x)\) and \(g(x)\) are continuous functions on \((-\infty, b)\) and the limits \(\lim_{x\to \infty}\) are replaced with \(\lim_{x \to -\infty}\). □
Example. Let us find \(\lim_{x\to\infty}x^{\frac{1}{x}}\).
Let \(f(x) = x^{\frac{1}{x}}\) so that \(\log f(x) = \frac{\log x}{x}\). Now consider \(\lim_{x\to\infty}\log f(x)\).
\(\lim_{x\to\infty}\log x = \infty\) and \(\lim_{x\to\infty}x = \infty\).
Defining the birth process Consider a colony of bacteria that never dies. We study the following process known as the birth process , also known as the Yule process . The colony starts with \(n_0\) cells at time \(t = 0\). Assume that the probability that any individual cell divides in the time interval \((t, t + \delta t)\) is proportional to \(\delta t\) for small \(\delta t\). Further assume that each cell division is independent of others. Let \(\lambda\) be the birth rate. The probability of a cell division for a population of \(n\) cells during \(\delta t\) is \(\lambda n \delta t\). We assume that the probability that two or more births take place in the time interval \(\delta t\) is \(o(\delta t)\). That is, it can be ignored. Consequently, the probability that no cell divides during \(\delta t\) is \(1 - \lambda n \delta t - o(\delta t)\). Note that this process is an example of the Markov chain with states \({n_0}, {n_0 + 1}, {n_0 + 2}...
Sometimes, we may simplify integration by using the product rule of differentiation. This technique is called integration by parts. Theorem (Integration by parts) Let \(f(x)\) and \(g(x)\) be differentiable functions on an open interval \(I\). Then, \(\int f(x)g'(x)dx = f(x)g(x) - \int f'(x)g(x)dx\); For any \(a, b \in I\), \[\int_a^bf(x)g'(x)dx = \left[f(x)g(x)\right]_a^b - \int_a^bf'(x)g(x)dx.\] Proof . By the product rule, \[[f(x)g(x)]' = f'(x)g(x) + f(x)g'(x)\] so \[f(x)g'(x) = [f(x)g(x)]' - f'(x)g(x).\] By integrating both sides, we have the desired results. ■ Example . Let us find \(\int x\cosh x dx\). \[ \begin{eqnarray*} \int x\cosh x dx &=& \int x(\sinh x)'dx \\ &=& x \sinh x - \int 1 \cdot \sinh x dx\\ &=& x \sinh x - \cosh x + C. \end{eqnarray*} \] Example (eg:recur) . Let us study how we can compute \[I_n = \int \frac{dx}{(x^2 + 1)^n}\] for \(n\in \mathbb{N}\). Note \[I_{n} = \int \fr...
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...
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