diff --git a/examples/case_studies/putting_workflow.ipynb b/examples/case_studies/putting_workflow.ipynb
index 3c590b485..a2df0c5da 100644
--- a/examples/case_studies/putting_workflow.ipynb
+++ b/examples/case_studies/putting_workflow.ipynb
@@ -173,7 +173,7 @@
"\\text{num. successes} \\sim \\operatorname{Binomial}(\\text{tries}, p(\\text{success}))\n",
"$$\n",
"\n",
- "Here is how to write that model in PyMC. We use underscore appendices in our model variables to avoid polluting the namespace. We also use {class}`pymc.MutableData` to let us swap out the data later, when we will work with a newer data set."
+ "Here is how to write that model in PyMC. We use underscore appendices in our model variables to avoid polluting the namespace. We also use {class}`pymc.Data` to let us swap out the data later, when we will work with a newer data set."
]
},
{
@@ -183,7 +183,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": "\n\n\n\n\n",
+ "image/svg+xml": "\n\n\n\n\n",
"text/plain": [
""
]
@@ -195,9 +195,9 @@
],
"source": [
"with pm.Model() as logit_model:\n",
- " distance_ = pm.MutableData(\"distance\", golf_data[\"distance\"], dims=\"obs_id\")\n",
- " tries_ = pm.MutableData(\"tries\", golf_data[\"tries\"], dims=\"obs_id\")\n",
- " successes_ = pm.MutableData(\"successes\", golf_data[\"successes\"], dims=\"obs_id\")\n",
+ " distance_ = pm.Data(\"distance\", golf_data[\"distance\"], dims=\"obs_id\")\n",
+ " tries_ = pm.Data(\"tries\", golf_data[\"tries\"], dims=\"obs_id\")\n",
+ " successes_ = pm.Data(\"successes\", golf_data[\"successes\"], dims=\"obs_id\")\n",
"\n",
" a_ = pm.Normal(\"a\")\n",
" b_ = pm.Normal(\"b\")\n",
@@ -535,7 +535,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": "\n\n\n\n\n",
+ "image/svg+xml": "\n\n\n\n\n",
"text/plain": [
""
]
@@ -552,9 +552,9 @@
"\n",
"\n",
"with pm.Model() as angle_model:\n",
- " distance_ = pm.MutableData(\"distance\", golf_data[\"distance\"], dims=\"obs_id\")\n",
- " tries_ = pm.MutableData(\"tries\", golf_data[\"tries\"], dims=\"obs_id\")\n",
- " successes_ = pm.MutableData(\"successes\", golf_data[\"successes\"], dims=\"obs_id\")\n",
+ " distance_ = pm.Data(\"distance\", golf_data[\"distance\"], dims=\"obs_id\")\n",
+ " tries_ = pm.Data(\"tries\", golf_data[\"tries\"], dims=\"obs_id\")\n",
+ " successes_ = pm.Data(\"successes\", golf_data[\"successes\"], dims=\"obs_id\")\n",
"\n",
" variance_of_shot = pm.HalfNormal(\"variance_of_shot\")\n",
" p_goes_in = pm.Deterministic(\n",
@@ -1002,7 +1002,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": "\n\n\n\n\n",
+ "image/svg+xml": "\n\n\n\n\n",
"text/plain": [
""
]
@@ -1018,9 +1018,9 @@
"\n",
"\n",
"with pm.Model() as distance_angle_model:\n",
- " distance_ = pm.MutableData(\"distance\", new_golf_data[\"distance\"], dims=\"obs_id\")\n",
- " tries_ = pm.MutableData(\"tries\", new_golf_data[\"tries\"], dims=\"obs_id\")\n",
- " successes_ = pm.MutableData(\"successes\", new_golf_data[\"successes\"], dims=\"obs_id\")\n",
+ " distance_ = pm.Data(\"distance\", new_golf_data[\"distance\"], dims=\"obs_id\")\n",
+ " tries_ = pm.Data(\"tries\", new_golf_data[\"tries\"], dims=\"obs_id\")\n",
+ " successes_ = pm.Data(\"successes\", new_golf_data[\"successes\"], dims=\"obs_id\")\n",
"\n",
" variance_of_shot = pm.HalfNormal(\"variance_of_shot\")\n",
" variance_of_distance = pm.HalfNormal(\"variance_of_distance\")\n",
@@ -1227,7 +1227,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": "\n\n\n\n\n",
+ "image/svg+xml": "\n\n\n\n\n",
"text/plain": [
""
]
@@ -1239,10 +1239,10 @@
],
"source": [
"with pm.Model() as disp_distance_angle_model:\n",
- " distance_ = pm.MutableData(\"distance\", new_golf_data[\"distance\"], dims=\"obs_id\")\n",
- " tries_ = pm.MutableData(\"tries\", new_golf_data[\"tries\"], dims=\"obs_id\")\n",
- " successes_ = pm.MutableData(\"successes\", new_golf_data[\"successes\"], dims=\"obs_id\")\n",
- " obs_prop_ = pm.MutableData(\n",
+ " distance_ = pm.Data(\"distance\", new_golf_data[\"distance\"], dims=\"obs_id\")\n",
+ " tries_ = pm.Data(\"tries\", new_golf_data[\"tries\"], dims=\"obs_id\")\n",
+ " successes_ = pm.Data(\"successes\", new_golf_data[\"successes\"], dims=\"obs_id\")\n",
+ " obs_prop_ = pm.Data(\n",
" \"obs_prop\", new_golf_data[\"successes\"] / new_golf_data[\"tries\"], dims=\"obs_id\"\n",
" )\n",
"\n",
diff --git a/examples/case_studies/putting_workflow.myst.md b/examples/case_studies/putting_workflow.myst.md
index a896273e7..f83794fac 100644
--- a/examples/case_studies/putting_workflow.myst.md
+++ b/examples/case_studies/putting_workflow.myst.md
@@ -128,13 +128,13 @@ p(\text{success}) = \operatorname{logit}^{-1}(a \cdot \text{distance} + b) \\
\text{num. successes} \sim \operatorname{Binomial}(\text{tries}, p(\text{success}))
$$
-Here is how to write that model in PyMC. We use underscore appendices in our model variables to avoid polluting the namespace. We also use {class}`pymc.MutableData` to let us swap out the data later, when we will work with a newer data set.
+Here is how to write that model in PyMC. We use underscore appendices in our model variables to avoid polluting the namespace. We also use {class}`pymc.Data` to let us swap out the data later, when we will work with a newer data set.
```{code-cell} ipython3
with pm.Model() as logit_model:
- distance_ = pm.MutableData("distance", golf_data["distance"], dims="obs_id")
- tries_ = pm.MutableData("tries", golf_data["tries"], dims="obs_id")
- successes_ = pm.MutableData("successes", golf_data["successes"], dims="obs_id")
+ distance_ = pm.Data("distance", golf_data["distance"], dims="obs_id")
+ tries_ = pm.Data("tries", golf_data["tries"], dims="obs_id")
+ successes_ = pm.Data("successes", golf_data["successes"], dims="obs_id")
a_ = pm.Normal("a")
b_ = pm.Normal("b")
@@ -271,9 +271,9 @@ def phi(x):
with pm.Model() as angle_model:
- distance_ = pm.MutableData("distance", golf_data["distance"], dims="obs_id")
- tries_ = pm.MutableData("tries", golf_data["tries"], dims="obs_id")
- successes_ = pm.MutableData("successes", golf_data["successes"], dims="obs_id")
+ distance_ = pm.Data("distance", golf_data["distance"], dims="obs_id")
+ tries_ = pm.Data("tries", golf_data["tries"], dims="obs_id")
+ successes_ = pm.Data("successes", golf_data["successes"], dims="obs_id")
variance_of_shot = pm.HalfNormal("variance_of_shot")
p_goes_in = pm.Deterministic(
@@ -512,9 +512,9 @@ DISTANCE_TOLERANCE = 3.0
with pm.Model() as distance_angle_model:
- distance_ = pm.MutableData("distance", new_golf_data["distance"], dims="obs_id")
- tries_ = pm.MutableData("tries", new_golf_data["tries"], dims="obs_id")
- successes_ = pm.MutableData("successes", new_golf_data["successes"], dims="obs_id")
+ distance_ = pm.Data("distance", new_golf_data["distance"], dims="obs_id")
+ tries_ = pm.Data("tries", new_golf_data["tries"], dims="obs_id")
+ successes_ = pm.Data("successes", new_golf_data["successes"], dims="obs_id")
variance_of_shot = pm.HalfNormal("variance_of_shot")
variance_of_distance = pm.HalfNormal("variance_of_distance")
@@ -619,10 +619,10 @@ We follow approach 3, as in the Stan case study, and leave 1 as an exercise.
```{code-cell} ipython3
with pm.Model() as disp_distance_angle_model:
- distance_ = pm.MutableData("distance", new_golf_data["distance"], dims="obs_id")
- tries_ = pm.MutableData("tries", new_golf_data["tries"], dims="obs_id")
- successes_ = pm.MutableData("successes", new_golf_data["successes"], dims="obs_id")
- obs_prop_ = pm.MutableData(
+ distance_ = pm.Data("distance", new_golf_data["distance"], dims="obs_id")
+ tries_ = pm.Data("tries", new_golf_data["tries"], dims="obs_id")
+ successes_ = pm.Data("successes", new_golf_data["successes"], dims="obs_id")
+ obs_prop_ = pm.Data(
"obs_prop", new_golf_data["successes"] / new_golf_data["tries"], dims="obs_id"
)