Data Science in Theory and Practice. Maria Cristina Mariani
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Definition 2.13 (Positive definite matrix) A square
matrix is called positive definite if, for any vector nonidentically zero, we haveExample 2.8
Let
be a 2 by 2 matrixTo show that
is positive definite, by definitionTherefore,
is positive definite.Definition 2.14 (Positive semidefinite matrix) A matrix
is called positive semidefinite (or nonnegative definite) if, for any vector , we haveDefinition 2.15 (Negative definite matrix) A square
matrix is called negative definite if, for any vector nonidentically zero, we haveExample 2.9
Let
be a 2 by 2 matrixTo show that
is negative definite, by definitionTherefore,
is negative definite.Definition 2.16 (Negative semidefinite matrix) A matrix
is called negative semidefinite if, for any vector , we haveWe state the following theorem without proof.
Theorem 2.1
A 2 by 2 symmetric matrix
is:
1 positive definite if and only if and det
2 negative definite if and only if and det
3 indefinite if and only if det .
2.3 Random Variables and Distribution Functions
We begin this section with the definition of
‐algebra.Definition 2.17 (σ‐algebra) A
‐algebra is a collection of sets of satisfying the following condition:1 .
2 If then its complement .
3 If is a countable collection of sets in then their union .
Definition 2.18 (Measurable functions) A real‐valued function