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Comments on Finding a non-trivial, rigorous definition of a "full measure" subset of a set of function spaces which satisfies the following (pt. 2)?

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Finding a non-trivial, rigorous definition of a "full measure" subset of a set of function spaces which satisfies the following (pt. 2)?

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Suppose $n\in\mathbb{N}$ and $f:A\subseteq\mathbb{R}^{n}\to\mathbb{R}$ is a function, where $A$ and $f$ are Borel.

Question: Assuming $\mathrm{S}(n)=\left\{\mathfrak{F}_1:\mathfrak{F}_1\in\mathbb{R}^\mathrm{K},\mathrm{K}\subseteq\mathbb{R}^{n} \text{ and $\mathfrak{F}_1$ are Borel}\right\}$ is the set of all $f$, what is an example of a non-trivial, rigorous definition of a "full measure" subset of $\mathrm{S}(n)$ which can be used to prove the following? For each $n\in\mathbb{N}$:

If $F\subset\mathrm{S}(n)$ is the set of all $\dot{\mathbf{f}}\in\mathrm{S}(n)$, where $\mathcal{D}:=\mathrm{dom}(\dot{\mathbf{f}})$ there exists $(f_r,A_r),(g_v,B_v)\to(\dot{\mathbf{f}},\mathcal{D})$ (Definition 2.1) such that $m_{\dot{\mathbf{f}},\mathcal{D}}((f_r),(A_r);n)\neq m_{\dot{\mathbf{f}},\mathcal{D}}((g_v),(B_v);n)$ (Definition 2.2), then $F$ is a "full measure" subset of $\mathrm{S}(n)$.

This means the set of all $f$, where two or more sequences of bounded functions converging to $f$ have non-equivelant expected values (Definition $\S$2.2), is a "full measure" subset of all $f$. In other terms, the "new mean" (Definition $\S$2.2) of "almost all" $f$ are non-unique.

Note: See the attempt in Section $\S$3.


$\S$2. Definitions:

Definition $\S$2.1 (Sequence of Bounded Functions With Different Domains Converging to $f$)

The sequence of functions $(f_r)_{r\in\mathbb{N}}$, where $(A_r)_{r\in\mathbb{N}}$ is a sequence of bounded sets and $f_{r}:A_r\to\mathbb{R}$ is a bounded function, converges to $f:A\subseteq\mathbb{R}^{n}\to\mathbb{R}$ (i.e., $A$ and $f$ are Borel) when:

For any $(x_1,\cdots,x_n)\in A$, there exists an $n$-dimensional sequence $(s_1(r),\cdots,s_n(r))\in A_r$ such that $(s_1(r),\cdots,s_n(r))\to (x_1,\cdots,x_n)$ and $f_r(s_1(r),\cdots,s_n(r))\to f(x_1,\cdots,x_n)$.

This is equivalent to:

$${(f_r,A_r)\to(f,A)}$$

Definition $\S$2.2 (Mean of a Sequence of Bounded Functions With Different Domains Converging to $f$)

Hence, suppose:

  • $(f_r,A_r)\to(f,A)$ (Definition 2.1)
  • $|\cdot|$ is the absolute value
  • $\dim_{\text{H}}(\cdot)$ is the Hausdorff dimension
  • $\mathcal{H}^{\dim_{\text{H}}(\cdot)}(\cdot)$ is the Hausdorff measure in its dimension on the Borel $\sigma$-algebra
  • the integral of $f$ is defined w.r.t the Hausdorff measure in its dimension

The mean of $(f_r)_{r\in\mathbb{N}}$ is a real number $m_{f,A}((f_r),(A_r);n)$, when the following is true:

$$\scriptsize{\begin{align}&\forall(\epsilon>0)\exists(N\in\mathbb{N})\forall(r\in\mathbb{N})\left(r\ge N\Rightarrow\left|\frac{1}{{\mathcal{H}}^{\text{dim}_{\text{H}}(A_{r})}(A_{r})}\int_{A_{r}}f_{r}\, d{\mathcal{H}}^{\text{dim}_{\text{H}}(A_{r})}-m_{f,A}((f_r),(A_r);n)\right|< \epsilon\right) \end{align}}$$

when no such $m_{f,A}((f_r),(A_r);n)$ exists, $m_{f,A}((f_r),(A_r);n)$ is infinite or undefined. (If the graph of $f$ has zero Hausdorff measure in its dimension, replace $\mathcal{H}^{\text{dim}_{\text{H}}(A_{r})}$ with the generalized Hausdorff measure ${\mathscr{H}^{\phi_{h,g}^{\mu}(q,t)}}$.)


$\S$3. Attempt

We want a "full measure" or "measure zero" set $F\subset \mathrm{S}(n)$ to be similar to a prevalent or shy subset of $\mathrm{S}(n)$; however, $\mathrm{S}(n)$ is not a vector space. Despite this, we can use the cardinality of a set $|\cdot|$ such that:

  1. If $F\subset \mathrm{S}(n)$ is a set and $|\mathrm{S}(n)\setminus F|<|\mathrm{S}(n)|$, then $F$ is a "full measure" subset of $\mathrm{S}(n)$
  2. If $F\subset \mathrm{S}(n)$ is a set and $|F|<|\mathrm{S}(n)|$, then $F$ is a "measure zero" subset of $\mathrm{S}(n)$

Still, when Borel $A\subseteq\mathbb{R}$ is a fixed set (i.e., $A=\mathbb{R}$) and $\mathrm{S}(n)\mapsto \mathbb{R}^{A}$, the quote is true when $F$ is prevalent.

If $F\subset\mathbb{R}^{A}$ is the set of all $f\in\mathbb{R}^{A}$, where there exists $(f_r,A_r),(g_v,B_v)\to(f,A)$ (Definition 2.1) such that $m_{f,A}((f_r),(A_r);n)\neq m_{f,A}((g_v),(B_v);n)$ (Definition 2.2) is finite, then $F$ is a prevalent subset of $\mathbb{R}^{A}$.

On the other hand, if we used the cardinality of a set, then 1. and 2. are false in order for the quote to be true and $F$ is neither a "measure zero" nor "full measure" subset of $\mathbb{R}^{A}$.

Sub-question: Can we define a "full measure" or "measure zero" subset of $\mathrm{S}(n)$ similar to a prevalent or shy set? (The measures should be the same as the ones answering the sub-questions here and here.) ${}{}{}{}{}$

History

1 comment thread

Regarding the post... (7 comments)
Regarding the post...
bharathk98‭ wrote 1 day ago

Does this post make sense?

clemens‭ wrote about 23 hours ago

Hello again!

On readability: I shall repeat my advice that, since you are looking for a single measure that satisfies three conditions, the conditions ought to be merged into a single question. Otherwise it adds unneeded difficulty for readers to go back and forth from one question to another, when the answer to all the questions will have to be the same.

I have said before that the mean of $f$ will depend on the marginal probability distribution on $f(x), x \in K$. For example, if $f(x) \sim \mathcal(N)(\mu,\sigma)$ then the mean of almost all $f$ will be $\mu$. So for the purposes of this question, it is also necessary to clarify why we want a particular marginal distribution for $f(x)$ and not another marginal distribution.

(Another way to think about it: In general we think of a probability distribution as an infinite-information analogue, so to speak, of an optimal encoding/Huffman tree. So what is the encoding, here?)

bharathk98‭ wrote about 19 hours ago · edited about 19 hours ago

clemens‭ I combined everything into one post. I do not know much about advanced math and statistics, but I think I want a uniform marginal probability distribution. If this makes no sense, consider the following:

I want an analytical definition, similar to prevalent and shy sets. Since $S(n)$ is not a vector space, this is not possible. However, I hope another analytical definition exists.

Let me know if the new post needs improvements.

clemens‭ wrote about 18 hours ago

OK, that's quite helpful.

You cannot have a uniform marginal probability distribution over the unbounded real numbers, though, any more than you can have a uniform probability distribution over the positive integers.

I'll try to look into the paper on prevalence you linked. Is it your understanding that that paper would solve your problem, if it weren't for having to use functions with bounded domains?

If so, then what weighting do you give functions on smaller domains than usual? E.g. let's say that $K = \mathbb{R}$; what's the probability that a random partial function on $K$ has a domain $\subseteq \mathbb{R}^+$?

bharathk98‭ wrote about 16 hours ago · edited about 15 hours ago

I'm not sure. According to Google AI, if $K$ is fixed, then $S(n)$ is a function space and must usually be a Polish space (complete, separable metric space) or a topological vector space. I know when $K$ is an interval, $S(n)$ has a prevalent subset.

Otherwise, when $K$ isn't fixed, $S(n)$ can never be a vector space: i.e., a prevalent subset of $S(n)$ does not exist.

Regarding your comment: when $K=\mathbb{R}$, the probability that a random partial function on $K$ has a domain $\subseteq\mathbb{R}^{+}$ should be $1/2$. Same, when $K$ has a domain $\subseteq(a,+\infty)$, for all $a>0$.

clemens‭ wrote about 15 hours ago · edited about 15 hours ago

This weighting of domains does not work. For then by symmetry a random partial function on $\mathbb{R}$ has domain $\subseteq \mathbb{R}^+$ with probability ½ and domain $\subseteq \mathbb{R}^-$ with probability ½. Since those two are disjoint, almost all partial functions on $\mathbb{R}$ would then have domain either $\subseteq \mathbb{R}^+$ or $\subseteq \mathbb{R}^-$.

(Besides pathological cases of this kind, I really do not expect that changing domains is going to meaningfully affect which sets are prevalent or shy. But what do you think?)

bharathk98‭ wrote about 14 hours ago · edited about 14 hours ago

According to the users on math stack exchange, when $K$ is an arbitrary subset of $\mathbb{R}$, the paper does not show a clear way of finding a prevalent or shy subset of $\mathbb{R}^{K}$.