Fix doc mistakes

blackjax/adaptation/meads_adaptation.py

@@ -36,7 +36,7 @@ class MEADSAdaptationState(NamedTuple):
     alpha
         Value of the alpha parameter of the generalized HMC algorithm.
     delta
-        Value of the alpha parameter of the generalized HMC algorithm.
+        Value of the delta parameter of the generalized HMC algorithm.
 
     """
 
@@ -60,7 +60,7 @@ def base():
     with shape.
 
     This is an implementation of Algorithm 3 of :cite:p:`hoffman2022tuning` using cross-chain
-    adaptation instead of parallel ensample chain adaptation.
+    adaptation instead of parallel ensemble chain adaptation.
 
     Returns
     -------

blackjax/base.py

@@ -89,7 +89,7 @@ class SamplingAlgorithm(NamedTuple):
     """A pair of functions that represents a MCMC sampling algorithm.
 
     Blackjax sampling algorithms are implemented as a pair of pure functions: a
-    kernel, that takes a new samples starting from the current state, and an
+    kernel, that generates a new sample from the current state, and an
     initialization function that creates a kernel state from a chain position.
 
     As they represent Markov kernels, the kernel functions are pure functions

blackjax/vi/svgd.py

@@ -135,7 +135,7 @@ def as_top_level_api(
     kernel: Callable = rbf_kernel,
     update_kernel_parameters: Callable = update_median_heuristic,
 ):
-    """Implements the (basic) user interface for the svgd algorithm.
+    """Implements the (basic) user interface for the svgd algorithm :cite:p:`liu2016stein`.
 
     Parameters
     ----------

docs/examples/howto_custom_gradients.md

@@ -29,10 +29,9 @@ Functions can be defined as the minimum of another one, $f(x) = min_{y} g(x,y)$.
 Our example is taken from the theory of [convex conjugates](https://en.wikipedia.org/wiki/Convex_conjugate), used for example in optimal transport. Let's consider the following function:
 
 $$
-\begin{align*}
-g(x, y) &= h(y) - \langle x, y\rangle\\
-h(x) &= \frac{1}{p}|x|^p,\qquad p > 1.\\
-\end{align*}
+\begin{equation*}
+g(x, y) = h(y) - \langle x, y\rangle,\qquad h(x) = \frac{1}{p}|x|^p,\qquad p > 1.
+\end{equation*}
 $$
 
 And define the function $f$ as $f(x) = -min_y g(x, y)$ which we can be implemented as:
@@ -69,7 +68,7 @@ Note the we also return the value of $y$ where the minimum of $g$ is achieved (t
 
 ### Trying to differentate the function with `jax.grad`
 
-The gradient of the function $f$ is undefined for JAX, which cannot differentiate through `while` loops, and trying to compute it directly raises an error:
+The gradient of the function $f$ is undefined for JAX, which cannot differentiate through `while` loops used in BFGS, and trying to compute it directly raises an error:
 
 ```{code-cell} ipython3
 # We only want the gradient with respect to `x`
@@ -97,15 +96,15 @@ The first order optimality criterion
 \end{equation*}
 ```
 
-Ensures that:
+ensures that
 
 ```{math}
 \begin{equation*}
     \frac{df}{dx} = y(x).
 \end{equation*}
 ```
 
-i.e. the value of the derivative at $x$ is the value $y(x)$ at which the minimum of the function $g$ is achieved.
+In other words, the value of the derivative at $x$ is the value $y(x)$ at which the minimum of the function $g$ is achieved.
 
 
 ### Telling JAX to use a custom gradient