Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Complexity theory of real functions
Complexity theory of real functions
Dynamical systems, measures, and fractals via domain theory
Information and Computation
Computability on random variables
Theoretical Computer Science - Special issue on computability and complexity in analysis
Computability on the probability measures on the Borel sets of the unit interval
Theoretical Computer Science - Special issue on computability and complexity in analysis
Computable analysis: an introduction
Computable analysis: an introduction
Computable Riesz Representation for Locally Compact Hausdorff Spaces
Electronic Notes in Theoretical Computer Science (ENTCS)
Notions of Probabilistic Computability on Represented Spaces
Electronic Notes in Theoretical Computer Science (ENTCS)
Absolutely non-computable predicates and functions in analysis†
Mathematical Structures in Computer Science
A computable approach to measure and integration theory
Information and Computation
An Application of Martin-Löf Randomness to Effective Probability Theory
CiE '09 Proceedings of the 5th Conference on Computability in Europe: Mathematical Theory and Computational Practice
Absolutely non-effective predicates and functions in computable analysis
TAMC'07 Proceedings of the 4th international conference on Theory and applications of models of computation
Computability of the Radon-Nikodym derivative
CiE'11 Proceedings of the 7th conference on Models of computation in context: computability in Europe
Computability on random events and variables in a computable probability space
Theoretical Computer Science
| Hi-index | 5.23 |
For every measure µ, the integral I: f ↦ ∫ f dµ is a linear functional on the set of real measurable functions. By the Daniell-Stone theorem, for every abstract integral Λ: F → R on a stone vector lattice F of real functions f: Ω → R there is a measure µ such that ∫ f dµ = Λ(f) for all f ∈ F. In this paper we prove a computable version of this theorem.