Chebyshev's inequality says: P(|X-μ| ≥ kσ) ≤ 1/k²

the union bound says: P(A∪B) ≤ P(A) + P(B)

The union bound states: P(A∪B) ≤ P(A) + P(B)

KL divergence is always ≥ 0 and equals 0 only when P = Q exactly

KL divergence measures the difference between two distributions P and Q; it is always non-negative and zero if and only if P equals Q exactly

Law of large numbers

Law of large numbers: X̄_ n → μ as n → ∞ with probability 1

Norm (mathematics)

L∞ norm equals max absolute value

Chebyshev's inequality

Chebyshev's inequality limits the probability of deviation from the mean

the L1 norm is not differentiable at zero

The L1 norm is not differentiable at zero because the absolute value function has a kink at zero