fix typos

elizavetasemenova · Nov 29, 2024 · eba59f9 · eba59f9
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diff --git a/07_MCMC.ipynb b/07_MCMC.ipynb
@@ -629,7 +629,7 @@
     "Which algorithm, in your opinion, is the most efficient?\n",
     "`````\n",
     "\n",
-    "For an-depth course on Monte Carlo, see {cite}`sanz2024first`."
+    "For an in-depth course on Monte Carlo, see {cite}`sanz2024first`."
    ]
   },
   {

diff --git a/09_intro_to_Numpyro.ipynb b/09_intro_to_Numpyro.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "# Introduction to NumPyro\n",
     "\n",
-    "[NumPyro](https://num.pyro.ai/en/latest/index.html#) {cite}`phan2019composable,bingham2019pyro` is a probabilistic programming library that combines the flexibility of `numpy` with the probabilistic modeling capabilities of `pyro`, making it an excellent choice for researchers and data scientists. In this introductory tutorial, we'll explore the basics of `numpyro` and how to get started with probabilistic programming in a hands-on manner."
+    "[NumPyro](https://num.pyro.ai/en/latest/index.html#) {cite}`phan2019composable,bingham2019pyro` is a probabilistic programming library that combines the flexibility of `numpy` with the probabilistic modelling capabilities of `pyro`, making it an excellent choice for researchers and data scientists. In this introductory tutorial, we'll explore the basics of `numpyro` and how to get started with probabilistic programming in a hands-on manner."
    ]
   },
   {
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -423,7 +423,7 @@
     "    # sum the log-probabilities across all data points\n",
     "    total_log_prob = jnp.sum(log_mixture_probs)\n",
     "    \n",
-    "    # sdd to the model using `numpyro.factor`\n",
+    "    # add to the model using `numpyro.factor`\n",
     "    numpyro.factor(\"custom_log_likelihood\", total_log_prob)\n",
     "\n",
     "\n",
@@ -440,7 +440,7 @@
    "source": [
     "## Summary\n",
     "\n",
-    "The typical elements that we will need to write are model in Numpyro are as follows:\n",
+    "The typical elements that we will need to write a model in Numpyro are as follows:\n",
     "\n",
     "- sample parameters with  <font color='green'>`numpyro.sample`</font>,\n",
     "- sample parameters from any of the built-in distributions using, e.g.  <font color='green'>`dist.Beta(alpha, beta)`</font>,\n",

diff --git a/09a_JAX_patterns.ipynb b/09a_JAX_patterns.ipynb
@@ -77,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use the option `in_axes` to specify along whch axis to apply the function. Not the difference with the `axes` option for the in-built operations in `numpy` and `jax.numpy:`"
+    "We can use the option `in_axes` to specify along which axis to apply the function. Not the difference with the `axes` option for the in-built operations in `numpy` and `jax.numpy:`"
    ]
   },
   {
@@ -202,7 +202,7 @@
    "source": [
     " Here `(0, 0, None)` tells `vmap` to apply the function `add_and_multiply_scalar` element-wise across the first dimension of `x` and `y`, while keeping the `scalar` fixed for each corresponding element-wise operation.\n",
     "\n",
-    " `vmap` applies a function to the elements of th leading axis of an array indendently. What if there is some carry over quantities that we need to use for the next copmutation based on last? Then we use `lax.scan`."
+    " `vmap` applies a function to the elements of the leading axis of an array indendently. What if there is some carry over quantities that we need to use for the next copmutation based on last? Then we use `lax.scan`."
    ]
   },
   {