Are all probability spaces topologies?

In summary, Rudin's Real & Complex Analysis discusses situations in probability theory where measures occur on spaces without topology or on topological spaces that are not locally compact. This can be seen in the example of the Weiner Measure, which occurs on a discrete probability space that is not locally compact. However, probability spaces still meet the criteria for a topology, but the point being made is that they are not necessarily defined by open sets like topological spaces are.
  • #1
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Rudin's Real & Complex Analysis makes this statement:

"... there are situations, especially in probability theory, where measures occur naturally on spaces without topology, or on topological spaces that are not locally compact. An example is the ... Weiner Measure"


A discrete probability space would not be locally compact, so that part I get

But given this definition of a topology:

A topological space is a set X together with T, a collection of subsets of X, satisfying the following axioms:

The empty set and X are in T.
The union of any collection of sets in T is also in T.
The intersection of any finite collection of sets in T is also in T.

Given that a probability space meets all these criteria, I do not understand the point above about "spaces without topology"
 
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  • #2
I agree with you, sigma-algebra satisfies all those axioms. I will take the book out next time I go on campus. Seems quite interesting.
 
  • #3
Probability spaces (like measure spaces) are closed under COUNTABLE unions and intersections. Topological spaces are defined by open sets, which are closed under ALL unions and FINITE intersections.
 

FAQ: Are all probability spaces topologies?

1. What is a probability space?

A probability space is a mathematical construct used to model random experiments or processes. It is defined by three components: a sample space, a set of events, and a probability measure.

2. What is a topology?

A topology is a mathematical structure that describes the properties of a set and its subsets. It is defined by a collection of open sets that satisfy certain axioms, such as closure under finite intersections and arbitrary unions.

3. How are probability spaces and topologies related?

Probability spaces and topologies are related in that a probability space is a special type of topological space. The sample space in a probability space can be seen as the underlying set of a topological space, with the events corresponding to the open sets. The probability measure then defines the probability of each event, which can be thought of as the measure of the corresponding open set in the topological space.

4. Are all probability spaces topologies?

No, not all probability spaces are topologies. To be a topology, a set of events must satisfy certain axioms, such as closure under finite intersections and arbitrary unions. Not all sets of events in a probability space will satisfy these axioms, therefore not all probability spaces can be considered as topologies.

5. Can topologies be used to study probability spaces?

Yes, topologies can be used to study probability spaces. The properties of topologies, such as being Hausdorff or compact, can provide insight into the behavior of a probability space. Additionally, tools from topology, such as convergence and continuity, can be applied to probability spaces to analyze their behavior and make predictions about future events.

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