Fix math rendering in variationally inferred parameterization tutorial (

#1767)

* fix math rendering in variationally inferred parameterization tutorial

* ruff format

notebooks/source/variationally_inferred_parameterization.ipynb

@@ -751,10 +751,10 @@
     "## **2. Model**\n",
     "We will be using a logistic regression model with hierarchical prior on coefficient scales\n",
     "\n",
-    "\\begin{aligned}\n",
+    "\\begin{align}\n",
     "\\log \\tau_0 & \\sim \\mathcal{N}(0,10) & \\log \\tau_i & \\sim \\mathcal{N}\\left(\\log \\tau_0, 1\\right) \\\\\n",
     "\\beta_i & \\sim \\mathcal{N}\\left(0, \\tau_i\\right) & y & \\sim \\operatorname{Bernoulli}\\left(\\sigma\\left(\\beta X^T\\right)\\right)\n",
-    "\\end{aligned}"
+    "\\end{align}"
    ]
   },
   {
@@ -1017,14 +1017,14 @@
    },
    "source": [
     "Thus, using the above transformation the joint density can be transformed as follows:\n",
-    "\\begin{aligned}\n",
+    "\\begin{align}\n",
     "p(\\theta, \\hat{\\mu}, \\mathbf{y}) & =\\mathcal{N}(\\theta \\mid 0,1) \\times\n",
     "\\mathcal{N}\\left(\\mu \\mid \\theta, \\sigma_\\mu\\right) \\times \\mathcal{N}(\\mathbf{y} \\mid \\mu, \\sigma)\n",
-    "\\end{aligned}\n",
+    "\\end{align}\n",
     "\n",
-    "\\begin{aligned}\n",
+    "\\begin{align}\n",
     "p(\\theta, \\hat{\\mu}, \\mathbf{y}) & =\\mathcal{N}(\\theta \\mid 0,1) \\times \\mathcal{N}\\left(\\hat{\\mu} \\mid \\lambda \\theta, \\sigma_\\mu^\\lambda\\right) \\times \\mathcal{N}\\left(\\mathbf{y} \\mid \\theta+\\sigma_\\mu^{1-\\lambda}(\\hat{\\mu}-\\lambda \\theta), \\sigma\\right)\n",
-    "\\end{aligned}\n"
+    "\\end{align}\n"
    ]
   },
   {