\( \newcommand{\BR}{\mathbb{R}} \newcommand{\non}{\nonumber} \newcommand{\al}{\alpha} \newcommand{\si}{\sigma} \DeclareMathOperator{\Var}{Var} \)

Problems

Problem 1. Calculate \(3 + 4^2\).

Solution. \(4^2 = 4\times 4 = 16\), therefore the answer is \(3 + 16 = 19\).

Problem 2. If \(\mathfrak A = 3\) and \(\mathbb{B} = -2\), find \(\mathfrak A + \mathbb{B}\).

Solution. \(3 + (-2) = 1\).

Goals

Students should know:

  1. How to count numbers

  2. How to add and subtract

  3. How to multiply and divide

\[\label{eq:special} \mathrm{d}W^2(t) = 2W(t)\mathrm{d}W(t) + \mathrm{d}t.\] This is known from [1].

These are our Examples

Math formulas

The mapping $\mathcal F : C(\mathbb R) \to \mathbb R$ is defined as $\mathcal F : F \mapsto F(0)$. The image $\mathfrak F := \mathcal F(C(\mathbb R))$ coincides with $\mathbb R$. We can use different notation in LaTeX such as $f, \mathbb f, \mathfrak f$, and all these are different letters.

\[\label{eq:new} x + y = z\] Consider the equation $\eqref{eq:new}$

Some Special Examples of Macros

$\RR$ is the set of all real numbers. Also it is denoted by $\BR$. $\bold{\alpha}$ is another example.

Picture!!

Brownian motion with drift g = 0.4 and diffusion \sigma = .5, starting from 0, 
and the drift line x = gt
Brownian motion with drift \(g = 0.4\) and diffusion \(\sigma = .5\), starting from \(0\), and the drift line \(x = gt\)

\(M/M/1\) Queue in an Interactive Jump Environment

Consider an \(M/M/1\) queue with an infinite waiting space operating in an interactive jump environment described as follows. Let \(\mathcal{Z}\) be a finite or countable state space. We define a two-component Markov process \(\mathcal{Y} = (X,Z)\) taking values in the countable state space \(\mathbb Z_+ \times \mathcal{Z}\) with the following generator matrix \(\mathbf{R}= \big( R[(n,z),(n',z')] \big)\): \[\begin{aligned} \label{Rmatrix-MM1} R[(n,z),(n+1, z)] &=& \alpha_z \lambda_z, \\ R[(n,z),(n-1, z)] &=& \alpha_z \mu_z, \\ R[(n,z),(n, z')] &=& \sigma_z \rho_z^{-n} \tau_n(z,z'), \\ R[(n,z),(n', z')] &=& 0, \non \\ R[(n,z),(n, z)] &=& - \sum_{(n',z') \in \mathbb Z_+ \times \mathcal{Z}\setminus \{(n,z)\}} R[(n,z),(n', z')], \non\end{aligned}\] where \(\rho_z:= \lambda_z/\mu_z\) for each \(z \in \mathcal{Z}\).

Consider the equation $\eqref{eq:special}$, which is known from .

Problem 3.3. \[\mathbb E X^2 = 1^2\cdot \frac16 + 2^2 \cdot \frac16 + \ldots + 6^2\cdot\frac16 = \frac{91}{6}\]

Problem 3.4. \[\Var X = \mathbb E(X^2) - (\mathbb E X)^2 = \frac{91}6 - \left(\frac72\right)^2 = \frac{35}{12}.\]

Problem 3.8. \(\mathbb E (2X - 4) = 2\mathbb E X - 4 = 2\cdot\frac72 - 4 = -3\).

Problem 3.11. \(\mathbb E(2X - 3Y) = 2\mathbb E X - 3\mathbb E Y = 2\frac72 - 3\frac72 = -\frac72\).

[1] Ioannis Karatzas, Steven E. Shreve (1991). Brownian Motion and Stochastic Calculus. Springer Lecture Notes in Mathematics 48.