Suppose you have continuous random variables \(X,Y\), you can use one to seed the value and the other to change the Gaussian distribution:

\begin{equation} p(x\mid y) = \mathcal{N}(x \mid my + b, \sigma^{2}) \end{equation}

A linearly independent list is a list of vectors such that there is one unique choice of scalars to be able to construct each member of their span.

Based on the same technique as in the proof that a sum of subsets is a direct sum IFF there is only one way to write \(0\), we can show that in a linearly independent list, there is (IFF) only one way to write the zero vector as a linear combination of that list of vectors —namely, the trivial representation of taking each vector to \(0\). In fact, we will actually use that as the formal definition of linear independence.

A Linear Map (a.k.a. Linear Transformation) is a function which maps elements between two vector space that follows linear properties.

constituents

• vector spaces \(V\) and \(W\) (they don’t have to be subspaces)
• A function \(T: V \to W\) (when we put something in, it only goes to one place)

requirements

\(T\) is considered a Linear Map if it follows… (properties of “linearity”)

additivity

\begin{equation} T(u+v) = Tu+Tv,\ \forall u,v \in V \end{equation}

homogeneity

\begin{equation} T(\lambda v) = \lambda (Tv),\ \forall \lambda \in \mathbb{F}, v \in V \end{equation}

general form of First-Order Differential Equations

This will depend on both unknown function \(x\), and the independent variable \(t\). These could and could not be separable.

\begin{equation} \dv{x}{t} = F(t,x),\ x(t_{0}) = x_{0} \end{equation}

Let’s imagine \(F\) is “bounded” and “continuous” on \(I \times \omega\), where \(I\) is an open interval about \(t_{0}\) and \(\omega\) is an open subset of \(\mathbb{R}^{n}\), containing \(x_{0}\). \(F\) is bounded; the results are bounded??

functions embedded in vector spaces

We understand that such First-Order Differential Equations will describe a subset of an infinite dimensional vector space.

Systems of Linear Equations

\begin{equation} T v = v' \end{equation}

every system of linear equations is decomposed into this. Classically, there’s either a unique solution, no solution, infinite solutions—

problems with zero

“zero” is really hard to define. For instance:

\begin{equation} 6.23423 \times 10^{192} - 1 \times 10^{7} = 6.23423 \times 10^{192} \end{equation}

so in this case \(10^{7}\) literally behaves like zero. (small numbers have the opposite problem)

so, we use elementary row operations to make sure that enormous numbers are essentially standardized—if a row has huge numbers, we may want to scale it down to smaller numbers to make them nice.