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๐Ÿ’ก 100% Bitcoin Allocation for Pensions Given medium-term problem horizon and ability to survive two Bitcoin halving cycles (eight years), 100% allocation is justified for pensions. Limited bailout availability argues for maximum volatility bet. From: bfi Learn more: Explore all courses:
๐Ÿ“– Definition A.4 (Existential Quantifier) If $P(x)$ is a property, then "$\\exists x, P(x)$" means "there exists at least one $x$ for which $P(x)$ is true." From: tao-analysis-1 Learn more: Explore all courses:
๐Ÿ“ Injectivity Criterion A linear transformation $T: V \\to W$ is injective (one-to-one) if and only if $\\ker(T) = \\{\\mathbf{0}\\}$. Proof: **(โ‡’)** If $T$ is injective and $v \\in \\ker(T)$, then $T(v) = \\mathbf{0} = T(\\mathbf{0})$. By injectivity, $v = \\mathbf{0}$. **(โ‡)** If $\\ker(T) = \\{\\mathbf{0}\\}$ and $T(u) = T(v)$, then $T(u - v) = \\mathbf{0}$, so $u - v \\in \\ker(T) = \\{\\mathbf{0}\\}$. Thus $u = v$. From: Advanced Linear Algebra Learn more: Explore all courses:
๐Ÿ“– Hierarchical Clustering Hierarchical clustering produces a tree-like dendrogram showing nested clusters. Bottom-up (agglomerative): start with $n$ clusters and merge. Top-down (divisive): start with 1 cluster and split. From: Intro to Statistical Learning Learn more: Explore all courses:
๐Ÿ“ Menger The maximum number of internally disjoint $u$-$v$ paths equals the minimum size of a $u$-$v$ separating set. Proof: The number of disjoint paths cannot exceed the separator size (each path needs a distinct separator vertex). For the reverse: induct on $|E|$. If every $u$-$v$ separator contains a neighbor of $u$ and a neighbor of $v$, delete an edge $e$ on a shortest path; the separator bound decreases by at mo... From: Introduction to Graph Theory Learn more: Explore all courses:
๐Ÿ“– Axiom 3.5 (Axiom of Specification) Let $A$ be a set, and for each $x \\in A$, let $P(x)$ be a property pertaining to $x$. Then there exists a set $\\{x \\in A : P(x)\\}$ whose elements are exactly those elements $x \\in A$ for which $P(x)$ is true. From: tao-analysis-1 Learn more: Explore all courses:
๐Ÿ’ก Proposition II.9 If a straight line be cut into equal and unequal segments, the squares on the unequal segments of the whole are double of the square on the half and of the square on the straight line between the points of section. From: Euclid's Elements Learn more: Explore all courses:
๐Ÿ“ Sample Theorem If $A \\subseteq B$ and $B \\subseteq A$, then $A = B$ Proof: Let $x \\in A$. Since $A \\subseteq B$, we have $x \\in B$ by definition of subset. Therefore, every element of $A$ is in $B$. Now, let $y \\in B$. Since $B \\subseteq A$, we have $y \\in A$ by definition. Therefore, every element of $B$ is in $A$. Since $A \\subseteq B$ and $B \\subseteq A... From: Numbers and Geometry Learn more: Explore all courses:
๐Ÿ“ Slope as Tangent If a line makes angle $\\theta$ with the positive $x$-axis, its slope is $m = \\tan\\theta$. Proof: Consider a line through the origin making angle $\\theta$ with the $x$-axis. A point on this line has coordinates $(r\\cos\\theta, r\\sin\\theta)$ for some $r > 0$. The slope is $m = \\frac{r\\sin\\theta - 0}{r\\cos\\theta - 0} = \\frac{\\sin\\theta}{\\cos\\theta} = \\tan\\theta$. From: Four Pillars of Geometry Learn more: Explore all courses:
๐Ÿ“– Survival Time The survival time $T$ is the time until an event of interest (death, failure, etc.) occurs. We observe $(Y_i, \\delta_i)$ where $Y_i = \\min(T_i, C_i)$ and $\\delta_i$ indicates if event was observed. From: Intro to Statistical Learning Learn more: Explore all courses:
๐Ÿ“– Definition of Inner Product Let $V$ be a real linear space. A function that assigns to each pair of elements $x$ and $y$ in $V$ a real number $(x, y)$, called the \\textbf{inner product}, satisfies: (1) $(x, y) = (y, x)$ (symmetry), (2) $(x + y, z) = (x, z) + (y, z)$ (additivity), (3) $(cx, y) = c(x, y)$ (homogeneity), (4) $(x, x) \\geq 0$, and $(x, x) = 0$ iff $x = 0$ (positivity). From: calc2-course Learn more: Explore all courses:
๐Ÿ“– Convolution When $X$ and $Y$ are independent with densities $f_X$ and $f_Y$, the density of $X + Y$ is the \\textbf{convolution}: $f_{X+Y}(u) = \\int_{-\\infty}^{\\infty} f_X(x) f_Y(u - x)\\,dx = (f_X * f_Y)(u)$. From: calc2-course Learn more: Explore all courses:
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