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diff --git a/source/know/concept/dynkins-formula/index.md b/source/know/concept/dynkins-formula/index.md
index 53da86f..c0d20c5 100644
--- a/source/know/concept/dynkins-formula/index.md
+++ b/source/know/concept/dynkins-formula/index.md
@@ -8,12 +8,12 @@ categories:
 layout: "concept"
 ---
 
-Given an [Itō diffusion](/know/concept/ito-calculus/) $X_t$
-with a time-independent drift $f$ and intensity $g$
-such that the diffusion uniquely exists on the $t$-axis.
-We define the **infinitesimal generator** $\hat{A}$
-as an operator with the following action on a given function $h(x)$,
-where $\mathbf{E}$ is a
+Given an [Itō diffusion](/know/concept/ito-calculus/) $$X_t$$
+with a time-independent drift $$f$$ and intensity $$g$$
+such that the diffusion uniquely exists on the $$t$$-axis.
+We define the **infinitesimal generator** $$\hat{A}$$
+as an operator with the following action on a given function $$h(x)$$,
+where $$\mathbf{E}$$ is a
 [conditional expectation](/know/concept/conditional-expectation/):
 
 $$\begin{aligned}
@@ -23,13 +23,13 @@ $$\begin{aligned}
     }
 \end{aligned}$$
 
-Which only makes sense for $h$ where this limit exists.
-The assumption that $X_t$ does not have any explicit time-dependence
-means that $X_0$ need not be the true initial condition;
-it can also be the state $X_s$ at any $s$ infinitesimally smaller than $t$.
+Which only makes sense for $$h$$ where this limit exists.
+The assumption that $$X_t$$ does not have any explicit time-dependence
+means that $$X_0$$ need not be the true initial condition;
+it can also be the state $$X_s$$ at any $$s$$ infinitesimally smaller than $$t$$.
 
-Conveniently, for a sufficiently well-behaved $h$,
-the generator $\hat{A}$ is identical to the Kolmogorov operator $\hat{L}$
+Conveniently, for a sufficiently well-behaved $$h$$,
+the generator $$\hat{A}$$ is identical to the Kolmogorov operator $$\hat{L}$$
 found in the [backward Kolmogorov equation](/know/concept/kolmogorov-equations/):
 
 $$\begin{aligned}
@@ -44,7 +44,7 @@ $$\begin{aligned}
 <label for="proof-kolmogorov">Proof</label>
 <div class="hidden" markdown="1">
 <label for="proof-kolmogorov">Proof.</label>
-We define a new process $Y_t \equiv h(X_t)$, and then apply Itō's lemma, leading to:
+We define a new process $$Y_t \equiv h(X_t)$$, and then apply Itō's lemma, leading to:
 
 $$\begin{aligned}
     \dd{Y_t}
@@ -53,7 +53,7 @@ $$\begin{aligned}
     &= \hat{L}\{h(X_t)\} \dd{t} + \pdv{h}{x} g(X_t) \dd{B_t}
 \end{aligned}$$
 
-Where we have recognized the definition of $\hat{L}$.
+Where we have recognized the definition of $$\hat{L}$$.
 Integrating the above equation yields:
 
 $$\begin{aligned}
@@ -63,14 +63,14 @@ $$\begin{aligned}
 
 As always, the latter [Itō integral](/know/concept/ito-integral/)
 is a [martingale](/know/concept/martingale/), so it vanishes
-when we take the expectation conditioned on the "initial" state $X_0$, leaving:
+when we take the expectation conditioned on the "initial" state $$X_0$$, leaving:
 
 $$\begin{aligned}
     \mathbf{E}[Y_t | X_0]
     = Y_0 + \mathbf{E}\bigg[ \int_0^t \hat{L}\{h(X_s)\} \dd{s} \bigg| X_0 \bigg]
 \end{aligned}$$
 
-For suffiently small $t$, the integral can be replaced by its first-order approximation:
+For suffiently small $$t$$, the integral can be replaced by its first-order approximation:
 
 $$\begin{aligned}
     \mathbf{E}[Y_t | X_0]
@@ -78,22 +78,23 @@ $$\begin{aligned}
 \end{aligned}$$
 
 Rearranging this gives the following,
-to be understood in the limit $t \to 0^+$:
+to be understood in the limit $$t \to 0^+$$:
 
 $$\begin{aligned}
     \hat{L}\{h(X_0)\}
     \approx \frac{1}{t} \mathbf{E}[Y_t - Y_0| X_0]
 \end{aligned}$$
+
 </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.
+and indeed, the generator $$\hat{A}$$ can be thought of as a differential operator.
 In that case, we would like an analogue of the classical
 fundamental theorem of calculus to relate it to integration.
 
 Such an analogue is provided by **Dynkin's formula**:
-for a stopping time $\tau$ with a finite expected value $\mathbf{E}[\tau|X_0] < \infty$,
+for a stopping time $$\tau$$ with a finite expected value $$\mathbf{E}[\tau|X_0] < \infty$$,
 it states that:
 
 $$\begin{aligned}
@@ -109,7 +110,7 @@ $$\begin{aligned}
 <div class="hidden" markdown="1">
 <label for="proof-dynkin">Proof.</label>
 The proof is similar to the one above.
-Define $Y_t = h(X_t)$ and use Itō’s lemma:
+Define $$Y_t = h(X_t)$$ and use Itō’s lemma:
 
 $$\begin{aligned}
     \dd{Y_t}
@@ -118,7 +119,7 @@ $$\begin{aligned}
     &= \hat{L} \{h(X_t)\} \dd{t} + \pdv{h}{x} g(X_t) \dd{B_t}
 \end{aligned}$$
 
-And then integrate this from $t = 0$ to the provided stopping time $t = \tau$:
+And then integrate this from $$t = 0$$ to the provided stopping time $$t = \tau$$:
 
 $$\begin{aligned}
     Y_\tau
@@ -127,7 +128,7 @@ $$\begin{aligned}
 
 All [Itō integrals](/know/concept/ito-integral/)
 are [martingales](/know/concept/martingale/),
-so the latter integral's conditional expectation is zero for the "initial" condition $X_0$.
+so the latter integral's conditional expectation is zero for the "initial" condition $$X_0$$.
 The rest of the above equality is also a martingale:
 
 $$\begin{aligned}
@@ -135,30 +136,30 @@ $$\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.
+Isolating this equation for $$\mathbf{E}[Y_\tau | X_0]$$ then gives Dynkin's formula.
 </div>
 </div>
 
 A common application of Dynkin's formula is predicting
-when the stopping time $\tau$ occurs, and in what state $X_\tau$ this happens.
+when the stopping time $$\tau$$ occurs, and in what state $$X_\tau$$ this happens.
 Consider an example:
-for a region $\Omega$ of state space with $X_0 \in \Omega$,
-we define the exit time $\tau \equiv \inf\{ t : X_t \notin \Omega \}$,
-provided that $\mathbf{E}[\tau | X_0] < \infty$.
+for a region $$\Omega$$ of state space with $$X_0 \in \Omega$$,
+we define the exit time $$\tau \equiv \inf\{ t : X_t \notin \Omega \}$$,
+provided that $$\mathbf{E}[\tau | X_0] < \infty$$.
 
-To get information about when and where $X_t$ exits $\Omega$,
-we define the *general reward* $\Gamma$ as follows,
-consisting of a *running reward* $R$ for $X_t$ inside $\Omega$,
-and a *terminal reward* $T$ on the boundary $\partial \Omega$ where we stop at $X_\tau$:
+To get information about when and where $$X_t$$ exits $$\Omega$$,
+we define the *general reward* $$\Gamma$$ as follows,
+consisting of a *running reward* $$R$$ for $$X_t$$ inside $$\Omega$$,
+and a *terminal reward* $$T$$ on the boundary $$\partial \Omega$$ where we stop at $$X_\tau$$:
 
 $$\begin{aligned}
     \Gamma
     = \int_0^\tau R(X_t) \dd{t} + \: T(X_\tau)
 \end{aligned}$$
 
-For example, for $R = 1$ and $T = 0$, this becomes $\Gamma = \tau$,
-and if $R = 0$, then $T(X_\tau)$ can tell us the exit point.
-Let us now define $h(X_0) = \mathbf{E}[\Gamma | X_0]$,
+For example, for $$R = 1$$ and $$T = 0$$, this becomes $$\Gamma = \tau$$,
+and if $$R = 0$$, then $$T(X_\tau)$$ can tell us the exit point.
+Let us now define $$h(X_0) = \mathbf{E}[\Gamma | X_0]$$,
 and apply Dynkin's formula:
 
 $$\begin{aligned}
@@ -168,11 +169,11 @@ $$\begin{aligned}
     &= \mathbf{E}\big[ T(X_\tau) | X_0 \big] + \mathbf{E}\bigg[ \int_0^\tau \hat{L}\{h(X_t)\} + R(X_t) \dd{t} \bigg| X_0 \bigg]
 \end{aligned}$$
 
-The two leftmost terms depend on the exit point $X_\tau$,
-but not directly on $X_t$ for $t < \tau$,
-while the rightmost depends on the whole trajectory $X_t$.
+The two leftmost terms depend on the exit point $$X_\tau$$,
+but not directly on $$X_t$$ for $$t < \tau$$,
+while the rightmost depends on the whole trajectory $$X_t$$.
 Therefore, the above formula is fulfilled
-if $h(x)$ satisfies the following equation and boundary conditions:
+if $$h(x)$$ satisfies the following equation and boundary conditions:
 
 $$\begin{aligned}
     \boxed{
@@ -183,8 +184,8 @@ $$\begin{aligned}
     }
 \end{aligned}$$
 
-In other words, we have just turned a difficult question about a stochastic trajectory $X_t$
-into a classical differential boundary value problem for $h(x)$.
+In other words, we have just turned a difficult question about a stochastic trajectory $$X_t$$
+into a classical differential boundary value problem for $$h(x)$$.