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authorPrefetch2022-10-27 20:40:09 +0200
committerPrefetch2022-10-27 20:40:09 +0200
commit6e70f28ccbd5afc1506f71f013278a9d157ef03a (patch)
treea8ca7113917f3e0040d6e5b446e4e41291fd9d3a /source/know/concept/dynkins-formula/index.md
parentbcae81336764eb6c4cdf0f91e2fe632b625dd8b2 (diff)
Optimize last images, add proof template, improve CSS
Diffstat (limited to 'source/know/concept/dynkins-formula/index.md')
-rw-r--r--source/know/concept/dynkins-formula/index.md23
1 files changed, 8 insertions, 15 deletions
diff --git a/source/know/concept/dynkins-formula/index.md b/source/know/concept/dynkins-formula/index.md
index c0d20c5..307f098 100644
--- a/source/know/concept/dynkins-formula/index.md
+++ b/source/know/concept/dynkins-formula/index.md
@@ -39,11 +39,8 @@ $$\begin{aligned}
}
\end{aligned}$$
-<div class="accordion">
-<input type="checkbox" id="proof-kolmogorov"/>
-<label for="proof-kolmogorov">Proof</label>
-<div class="hidden" markdown="1">
-<label for="proof-kolmogorov">Proof.</label>
+
+{% include proof/start.html id="proof-kolmogorov" -%}
We define a new process $$Y_t \equiv h(X_t)$$, and then apply Itō's lemma, leading to:
$$\begin{aligned}
@@ -84,9 +81,8 @@ $$\begin{aligned}
\hat{L}\{h(X_0)\}
\approx \frac{1}{t} \mathbf{E}[Y_t - Y_0| X_0]
\end{aligned}$$
+{% include proof/end.html id="proof-kolmogorov" %}
-</div>
-</div>
The general definition of resembles that of a classical derivative,
and indeed, the generator $$\hat{A}$$ can be thought of as a differential operator.
@@ -104,11 +100,8 @@ $$\begin{aligned}
}
\end{aligned}$$
-<div class="accordion">
-<input type="checkbox" id="proof-dynkin"/>
-<label for="proof-dynkin">Proof</label>
-<div class="hidden" markdown="1">
-<label for="proof-dynkin">Proof.</label>
+
+{% include proof/start.html id="proof-dynkin" -%}
The proof is similar to the one above.
Define $$Y_t = h(X_t)$$ and use Itō’s lemma:
@@ -136,9 +129,9 @@ $$\begin{aligned}
= \mathbf{E}\bigg[ Y_\tau - Y_0 - \int_0^\tau \hat{L}\{h(X_t)\} \dd{t} \bigg| X_0 \bigg]
\end{aligned}$$
-Isolating this equation for $$\mathbf{E}[Y_\tau | X_0]$$ then gives Dynkin's formula.
-</div>
-</div>
+Isolating this equation for $$\mathbf{E}[Y_\tau \!\mid\! X_0]$$ then gives Dynkin's formula.
+{% include proof/end.html id="proof-dynkin" %}
+
A common application of Dynkin's formula is predicting
when the stopping time $$\tau$$ occurs, and in what state $$X_\tau$$ this happens.