Probability measure on smooth functions

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SUMMARY

The discussion centers on the concept of defining a standard probability measure for the set of smooth real-valued functions on the interval [a, b]. Participants explore the idea of using Borel measure under the supremum metric, emphasizing the necessity of constraining functions to be uniformly bounded by a constant M to avoid infinite measure. The conversation highlights the importance of considering bounded variation to ensure the measure remains valid and equals one.

PREREQUISITES
  • Understanding of Borel measure
  • Familiarity with supremum metric
  • Knowledge of smooth functions and their properties
  • Concept of uniform boundedness in function spaces
NEXT STEPS
  • Research the properties of Borel measure in functional analysis
  • Study the implications of the supremum metric on function spaces
  • Explore the concept of uniform bounded variation in smooth functions
  • Investigate probability measures in the context of functional spaces
USEFUL FOR

Mathematicians, statisticians, and researchers in functional analysis or probability theory seeking to understand measures on smooth function spaces.

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Is there a "standard" probability measure one would use for the set of smooth real-valued functions on [a, b]?

My intuition is picturing a setup where you cut out shapes in the x-y plane, and then the set of functions whose graphs are contained in that shape have a measure proportional to the Euclidean area of the shape. But I can't quite make that intuition exact.
 
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Do you have the Borel measure ( under the sup metric ) in mind?
 
I suppose, you have to consider functions uniformely bounded by some constant M (or even vith uniformely bounded variation?), otherwise the whole set gets infinite measure, not 1, the way you described the measure.
 

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