Sub-probability measure (original) (raw)

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In the mathematical theory of probability and measure, a sub-probability measure is a measure that is closely related to probability measures. While probability measures always assign the value 1 to the underlying set, sub-probability measures assign a value lesser than or equal to 1 to the underlying set.

Let μ {\displaystyle \mu } be a measure on the measurable space ( X , A ) {\displaystyle (X,{\mathcal {A}})} .

Then μ {\displaystyle \mu } is called a sub-probability measure if μ ( X ) ≤ 1 {\displaystyle \mu (X)\leq 1} .[1][2]

In measure theory, the following implications hold between measures: probability ⟹ sub-probability ⟹ finite ⟹ σ -finite {\displaystyle {\text{probability}}\implies {\text{sub-probability}}\implies {\text{finite}}\implies \sigma {\text{-finite}}}

So every probability measure is a sub-probability measure, but the converse is not true. Also every sub-probability measure is a finite measure and a σ-finite measure, but the converse is again not true.

• Helly's selection theorem
• Helly–Bray theorem

1. ^ Klenke, Achim (2008). Probability Theory. Berlin: Springer. p. 247. doi:10.1007/978-1-84800-048-3. ISBN 978-1-84800-047-6.
2. ^ Kallenberg, Olav (2017). Random Measures, Theory and Applications. Switzerland: Springer. p. 30. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.