diff --git a/curvlinops/diagonal/hutchinson.py b/curvlinops/diagonal/hutchinson.py
index 1d82a52d..49c11d45 100644
--- a/curvlinops/diagonal/hutchinson.py
+++ b/curvlinops/diagonal/hutchinson.py
@@ -8,13 +8,32 @@
 
 
 class HutchinsonDiagonalEstimator:
-    """Class to perform diagonal estimation with Hutchinson's method.
+    r"""Class to perform diagonal estimation with Hutchinson's method.
 
     For details, see
 
     - Martens, J., Sutskever, I., & Swersky, K. (2012). Estimating the hessian by
       back-propagating curvature. International Conference on Machine Learning (ICML).
 
+    Let :math:`\mathbf{A}` be a square linear operator. We can approximate its diagonal
+    :math:`\mathrm{diag}(\mathbf{A})` by drawing a random vector :math:`\mathbf{v}`
+    which satisfies :math:`\mathbb{E}[\mathbf{v} \mathbf{v}^\top] = \mathbf{I}` and
+    sample from the estimator
+
+    .. math::
+        \mathbf{a}
+        := \mathbf{v} \odot \mathbf{A} \mathbf{v}
+        \approx \mathrm{diag}(\mathbf{A})\,.
+
+    This estimator is unbiased,
+
+    .. math::
+        \mathbb{E}[a_i]
+        = \sum_j \mathbb{E}[v_i A_{i,j} v_j]
+        = \sum_j A_{i,j} \mathbb{E}[v_i  v_j]
+        = \sum_j A_{i,j} \delta_{i, j}
+        = A_{i,i}\,.
+
     Example:
         >>> from numpy import diag, mean, round
         >>> from numpy.random import rand, seed
diff --git a/curvlinops/trace/hutchinson.py b/curvlinops/trace/hutchinson.py
index 4765dcbd..a2d1506c 100644
--- a/curvlinops/trace/hutchinson.py
+++ b/curvlinops/trace/hutchinson.py
@@ -7,7 +7,7 @@
 
 
 class HutchinsonTraceEstimator:
-    """Class to perform trace estimation with Hutchinson's method.
+    r"""Class to perform trace estimation with Hutchinson's method.
 
     For details, see
 
@@ -15,6 +15,25 @@ class HutchinsonTraceEstimator:
       matrix for laplacian smoothing splines. Communication in Statistics---Simulation
       and Computation.
 
+    Let :math:`\mathbf{A}` be a square linear operator. We can approximate its trace
+    :math:`\mathrm{Tr}(\mathbf{A})` by drawing a random vector :math:`\mathbf{v}`
+    which satisfies :math:`\mathbb{E}[\mathbf{v} \mathbf{v}^\top] = \mathbf{I}` and
+    sample from the estimator
+
+    .. math::
+        a
+        := \mathbf{v}^\top \mathbf{A} \mathbf{v}
+        \approx \mathrm{Tr}(\mathbf{A})\,.
+
+    This estimator is unbiased,
+
+    .. math::
+        \mathbb{E}[a]
+        = \mathrm{Tr}(\mathbb{E}[\mathbf{v}^\top\mathbf{A} \mathbf{v}])
+        = \mathrm{Tr}(\mathbf{A} \mathbb{E}[\mathbf{v} \mathbf{v}^\top])
+        = \mathrm{Tr}(\mathbf{A} \mathbf{I})
+        = \mathrm{Tr}(\mathbf{A})\,.
+
     Example:
         >>> from numpy import trace, mean, round
         >>> from numpy.random import rand, seed
diff --git a/curvlinops/trace/meyer2020hutch.py b/curvlinops/trace/meyer2020hutch.py
index f4fe7f8b..e37f32e3 100644
--- a/curvlinops/trace/meyer2020hutch.py
+++ b/curvlinops/trace/meyer2020hutch.py
@@ -10,7 +10,7 @@
 
 
 class HutchPPTraceEstimator:
-    """Class to perform trace estimation with the Huch++ method.
+    r"""Class to perform trace estimation with the Huch++ method.
 
     In contrast to vanilla Hutchinson, Hutch++ has lower variance, but requires more
     memory.
@@ -20,6 +20,26 @@ class HutchPPTraceEstimator:
     - Meyer, R. A., Musco, C., Musco, C., & Woodruff, D. P. (2020). Hutch++:
       optimal stochastic trace estimation.
 
+    Let :math:`\mathbf{A}` be a square linear operator whose trace we want to
+    approximate. First, we compute an orthonormal basis :math:`\mathbf{Q}` of a
+    sub-space spanned by :math:`\mathbf{A} \mathbf{S}` where :math:`\mathbf{S}` is a
+    tall random matrix with i.i.d. elements. Then, we compute the trace in the sub-space
+    and apply Hutchinson's estimator in the remaining space spanned by
+    :math:`\mathbf{I} - \mathbf{Q} \mathbf{Q}^\top`: We can draw a random vector
+    :math:`\mathbf{v}` which satisfies
+    :math:`\mathbb{E}[\mathbf{v} \mathbf{v}^\top] = \mathbf{I}` and sample from the
+    estimator
+
+    .. math::
+        a
+        := \mathrm{Tr}(\mathbf{Q}^\top \mathbf{A} \mathbf{Q})
+        + \mathbf{v}^\top (\mathbf{I} - \mathbf{Q} \mathbf{Q}^\top)^\top
+          \mathbf{A} (\mathbf{I} - \mathbf{Q} \mathbf{Q}^\top) \mathbf{v}
+        \approx \mathrm{Tr}(\mathbf{A})\,.
+
+    This estimator is unbiased, :math:`\mathbb{E}[a] = \mathrm{Tr}(\mathbf{A})`, as the
+    first term is constant and the second part is Hutchinson's estimator in a sub-space.
+
     Example:
         >>> from numpy import trace, mean, round
         >>> from numpy.random import rand, seed