DOC/BUG: fix covariance matrix notation in Nevo tutorial

docs/notebooks/tutorial/nevo.ipynb

@@ -117,16 +117,14 @@
     "It is common to assume that $f(\\beta_i \\mid \\theta)$ follows a multivariate normal distribution, and to break it up into three parts:\n",
     "\n",
     "1. A mean $K_1 \\times 1$ taste which all individuals agree on, $\\beta$.\n",
-    "2. A $K_2 \\times K_2$ covariance matrix, $\\Sigma$ .\n",
+    "2. A $K_2 \\times K_2$ covariance matrix, $V$. As is common with multivariate normal distributions, $V$ is not estimated directly. Rather, its matrix square (Cholesky) root $\\Sigma$ is estimated where $\\Sigma\\Sigma' = V$.\n",
     "3. Any $K_2 \\times D$ interactions, $\\Pi$, with observed $D \\times 1$ demographic data, $d_i$.\n",
     "\n",
     "Together this gives us that\n",
     "\n",
-    "$$\\beta_i \\sim N(\\beta + \\Pi d_i, \\Sigma).$$\n",
+    "$$\\beta_i \\sim N(\\beta + \\Pi d_i, \\Sigma\\Sigma').$$\n",
     "\n",
-    ":meth:`Problem.solve` takes an initial guess $\\Sigma_0$ of $\\Sigma$. It guarantees that $\\hat{\\Sigma}$ (the estimated parameters) will have the same sparsity structure as $\\Sigma_0$. So any zero element of $\\Sigma$ is restricted to be zero in the solution $\\hat{\\Sigma}$. For example, a popular restriction is that $\\Sigma$ is diagonal, this can be achieved by passing a diagonal matrix as $\\Sigma_0$.\n",
-    "\n",
-    "As is common with multivariate normal distributions, $\\Sigma$ is not estimated directly. Rather, its matrix square (Cholesky) root $L$ is estimated where $LL' = \\Sigma$."
+    ":meth:`Problem.solve` takes an initial guess $\\Sigma_0$ of $\\Sigma$. It guarantees that $\\hat{\\Sigma}$ (the estimated parameters) will have the same sparsity structure as $\\Sigma_0$. So any zero element of $\\Sigma$ is restricted to be zero in the solution $\\hat{\\Sigma}$. For example, a popular restriction is that $\\Sigma$ is diagonal, this can be achieved by passing a diagonal matrix as $\\Sigma_0$."
    ]
   },
   {
@@ -1275,7 +1273,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.9.13"
   },
   "pycharm": {
    "stem_cell": {