clear all warning

Author: AtsushiSakai

Localization/Kalmanfilter_basics.ipynb

@@ -108,7 +108,7 @@
     "\n",
     "This is for a multivariate distribution. For example, a robot in 2-D space can take values in both x and y. To describe them, a normal distribution with mean in both x and y is needed.\n",
     "\n",
-    "For a multivariate distribution, mean $\\mu can be represented as a matrix, \n",
+    "For a multivariate distribution, mean $\\mu$ can be represented as a matrix, \n",
     "\n",
     "$$\n",
     "\\mu = \\begin{bmatrix}\\mu_1\\\\\\mu_2\\\\ \\vdots \\\\\\mu_n\\end{bmatrix}\n",

Localization/Kalmanfilter_basics_2.ipynb

@@ -43,9 +43,9 @@
     "\n",
     "The filter works in two parts:\n",
     "\n",
-    "$\\bullet$  $p(x_{t} | x_{t-1},u_{t})\\rightarrow $**State Transition Probability**\n",
+    "$p(x_{t} | x_{t-1},u_{t})$ -> **State Transition Probability**\n",
     "\n",
-    "$\\bullet$  $p(z_{t} | x_{t}) \\rightarrow $**Measurement Probability**\n",
+    "$p(z_{t} | x_{t})$ -> **Measurement Probability**\n",
     "\n",
     "\n",
     "### Conditional dependence and independence example:\n",

docs/_static/.gitkeep

@@ -82,8 +82,8 @@ This is for a multivariate distribution. For example, a robot in 2-D
 space can take values in both x and y. To describe them, a normal
 distribution with mean in both x and y is needed.
 
-For a multivariate distribution, mean $:raw-latex:`\mu `can be
-represented as a matrix,
+For a multivariate distribution, mean :math:`\mu` can be represented as
+a matrix,
 
 .. math::
 

docs/modules/Kalmanfilter_basics.rst

@@ -42,12 +42,9 @@ complete.
 
 The filter works in two parts:
 
-:math:`\bullet` $p(x_{t} \|
-x_{t-1},u_{t}):raw-latex:`\rightarrow `$\ **State Transition
-Probability**
+:math:`p(x_{t} | x_{t-1},u_{t})` -> **State Transition Probability**
 
-:math:`\bullet` $p(z_{t} \| x_{t})
-:raw-latex:`\rightarrow `$\ **Measurement Probability**
+:math:`p(z_{t} | x_{t})` -> **Measurement Probability**
 
 Conditional dependence and independence example:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~