Integration with respect to the counting measure

In summary, the L^p norm of a measurable function on \mathbb{Z} with the counting measure is defined as the sum of the p-th power of the absolute values of the function's values, raised to the 1/p power. To prove this, one can use the monotone convergence theorem and break it down into simpler cases. Additionally, if the measure space is (\Omega, \mathcal{P}(\Omega)), then every function from \Omega to a topological space Y is measurable.
  • #1
AxiomOfChoice
533
1
I am struggling with convincing myself that if you equip [itex]\mathbb Z[/itex] with the counting measure [itex]m[/itex], the [itex]L^p[/itex] norm of measurable functions [itex]f: \mathbb Z \to \mathbb C[/itex] looks like
[tex]
\| f \|_p = \left( \sum_{n = -\infty}^\infty |a_n|^p \right)^{1/p}.
[/tex]
I know that any function on [itex]\mathbb Z[/itex] is essentially just a doubly-infinite sequence of complex numbers [itex]\left\{ \ldots, a_{-2}, a_{-1}, a_0, a_1, a_2, \ldots \right\}[/itex], with [itex]a_n = f(n)[/itex]. But how does one get from the general definition of integrals of positive functions (which [itex]|f|^p[/itex] certainly is) to the sum that appears above?
 
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  • #2
So you wish to prove that (if c is the counting measure)

[tex]\int fdc=\sum_{-\infty}^{+\infty}f(n)[/tex]

Let's do this in steps:

1) f is of the form [itex]I_{\{x\}}[/tex] (where [itex]I_A(z)=1[/itex] if [itex] z\in A[/itex] and 0 otherwise), then

[tex]\int f dc=\int I_{\{x\}}dc=c\{x\}=1[/tex].

2) f is of the form [itex]\sum_{k=-n}^n{a_k I_{\{x_k\}}}[/itex] (a finite combination of things in (1)). Then

[tex]\int fdc=\sum_{k=-n}^n a_k \int I_{\{x_k\}} = \sum_{k=-n}^n{a_k}[/tex]

3) If f is positive of the form [itex]\sum_{k=-\infty}^{+\infty} {a_k I_{\{x_k\}}}[/itex] (with [itex]a_k\geq 0[/itex]). Then we apply the monotone convergence theorem:

[tex]\int fdc= \sum_{k=-\infty}^{+\infty}{a_k}[/tex]

4) Finally, if f is integrable, then we can write [itex]f=f^+-f^-[/itex] and thus

[tex]\int fdc=\int f^+dc-\int f^-dc[/tex]
 
  • #3
micromass said:
So you wish to prove that (if c is the counting measure)

[tex]\int fdc=\sum_{-\infty}^{+\infty}f(n)[/tex]

Let's do this in steps:

1) f is of the form [itex]I_{\{x\}}[/tex] (where [itex]I_A(z)=1[/itex] if [itex] z\in A[/itex] and 0 otherwise), then

[tex]\int f dc=\int I_{\{x\}}dc=c\{x\}=1[/tex].

2) f is of the form [itex]\sum_{k=-n}^n{a_k I_{\{x_k\}}}[/itex] (a finite combination of things in (1)). Then

[tex]\int fdc=\sum_{k=-n}^n a_k \int I_{\{x_k\}} = \sum_{k=-n}^n{a_k}[/tex]

3) If f is positive of the form [itex]\sum_{k=-\infty}^{+\infty} {a_k I_{\{x_k\}}}[/itex] (with [itex]a_k\geq 0[/itex]). Then we apply the monotone convergence theorem:

[tex]\int fdc= \sum_{k=-\infty}^{+\infty}{a_k}[/tex]

4) Finally, if f is integrable, then we can write [itex]f=f^+-f^-[/itex] and thus

[tex]\int fdc=\int f^+dc-\int f^-dc[/tex]
Yeah. Thinking about it using the monotone convergence theorem the way you did definitely seems like the best approach. Thanks, micromass.

By the way...if your measure space is [itex](\Omega, \mathcal P(\Omega))[/itex] (a set and its power set), isn't every function [itex]f: \Omega \to Y[/itex], where [itex]Y[/itex] is some topological space, measurable?
 
  • #4
AxiomOfChoice said:
Yeah. Thinking about it using the monotone convergence theorem the way you did definitely seems like the best approach. Thanks, micromass.

By the way...if your measure space is [itex](\Omega, \mathcal P(\Omega))[/itex] (a set and its power set), isn't every function [itex]f: \Omega \to Y[/itex], where [itex]Y[/itex] is some topological space, measurable?

Yes, if your sigma-algebra is [itex]\mathcal{P}(Y)[/itex], then everything is measurable.
 

FAQ: Integration with respect to the counting measure

1. What is "integration with respect to the counting measure?"

Integration with respect to the counting measure is a method of calculating the area under a function by summing up the values of the function at each point on a discrete set of numbers. The counting measure assigns a weight of 1 to each point, making it a useful tool for counting and measuring discrete quantities.

2. How is integration with respect to the counting measure different from other methods of integration?

The main difference is that integration with respect to the counting measure is used for discrete sets of numbers, while other methods of integration are used for continuous functions. In integration with respect to the counting measure, the area under the function is calculated by summing up the values at each point, rather than using a continuous function to find the area.

3. What are some real-world applications of integration with respect to the counting measure?

Integration with respect to the counting measure is commonly used in statistics and probability to calculate the likelihood of events occurring. It is also used in computer science for data compression and in economics to calculate expected values and probabilities in decision-making processes.

4. What are the limitations of integration with respect to the counting measure?

Integration with respect to the counting measure is limited to discrete sets of numbers and cannot be used for continuous functions. It also assumes that all points have the same weight of 1, which may not accurately represent the function being analyzed. Additionally, it is not as precise as other methods of integration, such as the Riemann or Lebesgue integrals.

5. How is integration with respect to the counting measure related to the concept of summation?

Integration with respect to the counting measure is essentially a generalized form of summation, where the function being integrated is not necessarily a sequence of numbers. In fact, when the counting measure is applied to a discrete set of numbers, it yields the same result as a traditional summation of those numbers. This relationship makes integration with respect to the counting measure a powerful tool for solving problems involving discrete quantities.

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