Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Limit of a univariate function
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Let \(f(x)\) be a function. Suppose we move \(x\in\mathbb{R}\) towards \(a\) while keeping \(x \neq a\). If in this case \(f(x)\) approaches a constant value \(\alpha\) irrespective of the way how \(x\) approaches \(a\), we say that \(f(x)\) converges to \(\alpha\) as \(x \to a\) and write \[\lim_{x\to a}f(x) = \alpha\] or \[f(x) \to \alpha \text{ as \(x \to a\)}.\]
Remark. \(a\) needs not belong to \(\text{dom}(f)\) (the domain of \(f\)) as long as \(x\) can approach \(a\) arbitrarily closely. □
But what does this mean exactly? Here's a rigorous definition in terms of what is called the \(\varepsilon-\delta\) argument.
Definition (Limit of a function)
We say that the function \(f(x)\) converges to \(\alpha\) as \(x \to a\) and write
\[\lim_{x\to a}f(x) = \alpha\]
if the following condition is satisfied.
For any \(\varepsilon > 0\), there exists \(\delta > 0\) such that, for all \(x\in \text{dom}(f)\), if \(0 < |x - a| < \delta\) then \(|f(x) - \alpha| < \varepsilon\).
Remark. We are implicitly assuming \(\varepsilon, \delta \in\mathbb{R}\). □
Here's how this definition works. Suppose \(f(x) \to \alpha\) as \(x \to a\). Let us pick any positive real number \(\varepsilon\). However small this \(\varepsilon\) may be, if \(x\) is sufficiently close to \(a\), we can always have \(|f(x) - \alpha| < \varepsilon\). Here ``sufficiently close'' means that we can pick some sufficiently small positive real number \(\delta\) such that \(|x - a| < \delta\) implies \(|f(x) - \alpha| < \varepsilon\). In other words, we move \(x\) closer and closer to \(a\) until \(|f(x) - \alpha| < \varepsilon\) holds. Conversely, if this operation is possible for any \(\varepsilon\), it makes sense to say that \(f(x)\) converges to \(\alpha\) as \(x \to a\).
Example. Consider \(f(x) = x^2 + 1\). We have
\[\lim_{x \to 1}f(x) = 2.\]
Let \(\varepsilon = 0.1\). Let us find \(\delta > 0\) such that \(0 < |x - 1| < \delta\) implies \(|f(x) - 2| < \varepsilon\). Suppose
Since \(\sqrt{0.9} - 1 = -0.0513\cdots\) and \(\sqrt{1.1} - 1 = 0.0488\cdots\), if
\[|x - 1| < \sqrt{1.1} - 1,\]
then we have
\[|f(x) - 2| < 0.1 = \varepsilon.\]
So \(\delta\) can be any positive number less than \(\sqrt{1.1} - 1\). For example, let \(\delta = 0.04\). Then \(|x - 1| < 0.04\) implies \(0.96 < x < 1.04\), which implies \(-0.0784 < x^2 - 1 < 0.0816\) so
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 \(\mathbf{X} = (X_1, X_2, \cdots, X_n)^\top \in \mathbb{R}^n\) 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-...
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