Fix typo in math expression

Author: krasserm

gaussian-processes/gaussian_processes_classification.ipynb

@@ -84,7 +84,7 @@
  "\\tag{6}\n",
  "$$\n",
  "\n",
- "This integral is analytically intractable. Two approximation are needed to make it tractable. First, $p(a_*|\\mathbf{t})$ must be approximated with a Gaussian distribution. Second, $p(t_*=1 \\mid a_*) = \\sigma(a_*)$ must be approximated with the inverse probit function $\\Phi(a_*)$ (see also \\[2\\] section 4.3.5). Let's start with $p(a_* \\mid \\mathbf{t})$ which can be defined as:\n",
+ "This integral is analytically intractable. Two approximation are needed to make it tractable. First, $p(a_* \\mid \\mathbf{t})$ must be approximated with a Gaussian distribution. Second, $p(t_*=1 \\mid a_*) = \\sigma(a_*)$ must be approximated with the inverse probit function $\\Phi(a_*)$ (see also \\[2\\] section 4.3.5). Let's start with $p(a_* \\mid \\mathbf{t})$ which can be defined as:\n",
  "\n",
  "$$\n",
  "p(a_* \\mid \\mathbf{t}) = \\int{p(a_* \\mid \\mathbf{a}) p(\\mathbf{a} \\mid \\mathbf{t}) d\\mathbf{a}}\n",
@@ -440,7 +440,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "Computing class 1 probabilities at `X_test` gives the following output (green line), shown in context of the training data. The black dotted line represents the decision boundary at $p(t_*=1|\\mathbf{t}) = 0.5$."
+ "Computing class 1 probabilities at `X_test` gives the following output (green line), shown in context of the training data. The black dotted line represents the decision boundary at $p(t_*=1 \\mid \\mathbf{t}) = 0.5$."
  ]
  },
  {
@@ -690,7 +690,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "This is also the case for the predicted class 1 probabilities shown in the following contour plots. The black dashed line represents the decision boundary at $p(t_*=1|\\mathbf{t}) = 0.5$. The results obtained with the minimal implementation"
+ "This is also the case for the predicted class 1 probabilities shown in the following contour plots. The black dashed line represents the decision boundary at $p(t_*=1 \\mid \\mathbf{t}) = 0.5$. The results obtained with the minimal implementation"
  ]
  },
  {