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world:de-finetti [2026/02/06 13:12]
rdouc [Proof]
world:de-finetti [2026/02/07 12:44] (current)
rdouc
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-on{{page>:​defs}}+{{page>:​defs}}
  
  
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 <WRAP center round tip 80%> <WRAP center round tip 80%>
 **De Finetti'​s Theorem:​**  ​ **De Finetti'​s Theorem:​**  ​
-Let $(X_i)_{i\in\mathbb{N}}$ be a family of **exchangeable random elements** ​defined ​on a measurable space $(\mathsf{X},​\mathcal{X})$. Then, there exists a $\sigma$-field $\mathcal{G}_\infty$ such that, **conditionally on $\mathcal{G}_\infty$**,​ the random variables $(X_i)_{i\in\mathbb{N}}$ are **independent and identically distributed (i.i.d.)**.+Let $(X_i)_{i\in\mathbb{N}}$ be a family of **exchangeable random elements** ​taking values ​on a measurable space $(\mathsf{X},​\mathcal{X})$. Then, there exists a $\sigma$-field $\mathcal{G}_\infty$ such that, **conditionally on $\mathcal{G}_\infty$**,​ the random variables $(X_i)_{i\in\mathbb{N}}$ are **independent and identically distributed (i.i.d.)**.
  
 </​WRAP>​ </​WRAP>​
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 =\prod_{\ell=1}^k\mathbb{E}[f_\ell(X_1)\mid\mathcal{G}_\infty]. =\prod_{\ell=1}^k\mathbb{E}[f_\ell(X_1)\mid\mathcal{G}_\infty].
 $$ $$
-    * Thus, conditionally on $\mathcal{G}_\infty$,​ $(X_i)$ are independent.+    * Thus, conditionally on $\mathcal{G}_\infty$,​ $(X_i)$ are independent. ​$\blacksquare$
  
-==== Comments: ​convergence ​of Empirical Averages ​for i.i.d. Random Variables ====+<WRAP center round todo 80%> 
 +If $(X_i)$ are real-valued random variables, then, since the distribution of any random variable $X$ is completely determined by the values $\mathbb{P}(X \le x)$ for $x \in \mathbb{Q}$,​ we can replace $\mathcal{G}_\infty$ with the $\sigma$-field generated by the countable family of random variables 
 +\[ 
 +\left\{ \mathbb{E}\big[\mathbf{1}_{\{X_1 \le x\}} \mid \mathcal{G}_\infty \big] : x \in \mathbb{Q} \right\}. 
 +\] 
 +It follows that there exists a random variable $S$ such that, conditional on $S$, the sequence $(X_i)$ is independent and identically distributed.  
 + 
 +</​WRAP>​ 
 + 
 + 
 +==== Comments: ​Another proof of the Law of Large Numbers ​for i.i.d. Random Variables ====
  
-The previous approach allows to prove the strong law of large numbers+The previous approach allows to prove the strong law of large numbers ​(for a proof of the LLN using only the dominated convergence theorem, ​ [[world:​lln| click here]])
  
 <WRAP center round tip 80%> <WRAP center round tip 80%>
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-**Reverse Martingale Convergence Theorem:**+**Proof:**
 By the **reverse martingale convergence theorem** (see [[world:​forward-downward-martingale|Upcrossing Inequality and Martingale Convergence]]),​ the empirical average converges almost surely to the conditional expectation with respect to the tail σ-field \(\mathcal{G}_\infty\):​ By the **reverse martingale convergence theorem** (see [[world:​forward-downward-martingale|Upcrossing Inequality and Martingale Convergence]]),​ the empirical average converges almost surely to the conditional expectation with respect to the tail σ-field \(\mathcal{G}_\infty\):​
 \[ \[
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 \] \]
  
-**Hewitt-Savage 0-1 Law:** 
 The [[world:​hewitt-savage|Hewitt-Savage 0-1 Law]] states that any \(\mathcal{G}_\infty\)-measurable random variable is almost surely constant. Consequently,​ the conditional expectation \(\mathbb{E}[X_1|\mathcal{G}_\infty]\) is almost surely equal to its unconditional expectation:​ The [[world:​hewitt-savage|Hewitt-Savage 0-1 Law]] states that any \(\mathcal{G}_\infty\)-measurable random variable is almost surely constant. Consequently,​ the conditional expectation \(\mathbb{E}[X_1|\mathcal{G}_\infty]\) is almost surely equal to its unconditional expectation:​
 \[ \[
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 \] \]
  
-**Conclusion:​** 
 Thus, the empirical average converges almost surely to the theoretical expectation:​ Thus, the empirical average converges almost surely to the theoretical expectation:​
 \[ \[
world/de-finetti.1770379932.txt.gz · Last modified: 2026/02/06 13:12 by rdouc