Statistical Inference/Probability Theory
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< Statistical Inference
Set Theory
Sample Space [Definition 1.1.1]
The set, S, of all possible outcomes of a particular experiment is called the sample space for the experiment.
Event [Definition 1.1.2]
An event is any collection of possible outcomes of an experiment, that is, any subset of S (including S itself).
Let A be an event, a subset of S. We say the event A occurs if the outcome of the experiment is in the set A. When speaking of probabilities, we generally speak of the probability of an event, rather than a set. But we may use the terms interchangeably.
We first need to define formally the following two relationships, which allow us to order and equate sets:
Containment:
<amsmath>A \subset B \Leftrightarrow x \in A \Rightarrow x \in B</amsmath>
Equality:
<amsmath>A = B \Leftrightarrow A \subset B \land B \subset A</amsmath>
Given any two events (or sets) A and B , we have the following elementary set operations:
Union: The union of A and B, written <amsmath>A \cup B</amsmath>, is the set of elements that belong to either A or B or both:
<amsmath>A \cup B = \{ x : x \in A \lor x \in B \}</amsmath>
Intersection: The intersection of A and B, written <amsmath>A \cap B</amsmath>, is the set of elements that belong to both A and B:
<amsmath>A \cap B = \{ x : x \in A \land x \in B \}</amsmath>
Complementation: The complement of A, written <amsmath>A^c</amsmath>, is the set of all elements that are not in A:
<amsmath>A^c = \{ x : x \notin A \}</amsmath>