Random Measures, Theory and Applications

Kallenberg, O. (2017). Random Measures, Theory and Applications. Springer.

Type

Book

Intellectual base

Review or monograph

Year

2017

DOI

10.1007/978-3-319-41598-7

ISBN

9783319415963

DOI registrant

Springer Nature

Link

https://doi.org/10.1007/978-3-319-41598-7

Cited by

6 citations across 6 artifacts.

Domain-specific

• Equivalence (measure theory)
  • The move from On \([0,1]\), Lebesgue measure \(\mu\) and the measure \(\nu(A)=\int_A x^2\,d\mu\) are equivalent even though their values differ. to Two probability models with strictly positive densities relative to the same dominating measure belong to the same measure class. demonstrates that continuity.
• Measurable space
  • No numerical measure is included until one is assigned, and Borel space terminology can mean a topology-generated measurable space under stricter conventions.
• Random measure
  • The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.
• Stopping time
  • Stopping times formalize nonanticipating termination rules and support stopped processes, optional sampling, sequential decisions, hitting-time analysis, and stochastic control.
• Sub-probability measure
  • The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.
• Vague topology
  • The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.

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