Understanding some more set theory for statistics

In summary, the speaker is seeking recommendations for books on set theory, particularly with regards to probability theory. They mention having trouble understanding set theory in application and express interest in learning more about concepts such as sigma algebras and Polish spaces. They also mention the need to specify the level of set theory they want to study in order to receive helpful advice.
  • #1
universalis
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Hope this is the right forum for my question.

I'm into statistics and quite often see assumptions involving set theory. I know some set theory but am having trouble understanding it for any application. I would like to narrow this gap, maybe because this type of mathematics seems most interesting to me or Maybe because it seems so hard? Anyway, my problem when studying some books is that I'm having a hard time imagining any set theory than the most basic. For example, I've looked at descrptive set theory, it seemed hard though. Therefore I would like to ask you about any book you could recommend.

My question is a bit fuzzy but I hope you know what I mean. Thanks!
 
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  • #2
To get good advice, I think you must indicate the level of set theory you want to study.

Elementary probability theory uses concepts such as intersection, complement, union, De Morgan's laws.

It uses permutations and combinations of sets of things - Is that part of the set theory you want to study?

Advanced probability uses limits of sequences of sets, sigma algebras of sets.

It may use topological properties of sets such as "everywhere dense", "connected".

It may use properties of set cardinality such as "countably infinite".
 
  • #3
Yes, sigma algebras, filtrations, probability spaces, etc. are some of the things I would like to read more about. For example, what is meant by a Polish space being used as a state space.
 

1. What is set theory and why is it important in statistics?

Set theory is a branch of mathematics that deals with the study of sets, which are collections of objects. In statistics, set theory is important because it provides a framework for understanding and organizing data, allowing us to make meaningful comparisons and draw conclusions about the relationships between different sets of data.

2. What are the basic concepts of set theory that are relevant to statistics?

Some of the basic concepts of set theory that are relevant to statistics include union, intersection, complement, and subset. Union refers to the combination of two or more sets, intersection refers to the overlapping elements between two sets, complement refers to the elements that are not included in a set, and subset refers to a set that is contained within another set.

3. How can set theory be applied in statistical analysis?

Set theory can be applied in statistical analysis in various ways. For example, it can be used to define and describe sample spaces, which are all possible outcomes of a statistical experiment. It can also be used to define events and calculate probabilities of certain outcomes, and to compare and analyze data sets using set operations.

4. What is the difference between a finite and infinite set in set theory?

A finite set is a set that has a specific and limited number of elements, while an infinite set is a set that has an unlimited number of elements. In statistics, we often work with finite sets, as we are dealing with a limited amount of data. However, infinite sets can also be useful in certain statistical models and analyses.

5. How does set theory relate to other branches of mathematics in statistics?

Set theory is closely related to other branches of mathematics in statistics, such as probability theory, combinatorics, and algebra. These branches often use set operations and concepts to solve problems and make connections between different data sets. Additionally, set theory is also used in data visualization and data mining, which are important tools in statistical analysis.

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