Release 2026.5.1.1: bump Colab URL tag refs

Author: rhayes777

notebooks/chapter_1_introduction/README.rst

@@ -8,17 +8,17 @@ Files
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_1_models.ipynb>`_
+`Tutorial 1: Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_1_models.ipynb>`_
 - What probabilistic models are and how to compose them using PyAutoFit.
 
-`Tutorial 2: Fitting Data <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_2_fitting_data.ipynb>`_
+`Tutorial 2: Fitting Data <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_2_fitting_data.ipynb>`_
 - Fitting a model with an input set of parameters to data and quantifying the goodness of fit.
 
-`Tutorial 3: Non Linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb>`_
+`Tutorial 3: Non Linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb>`_
 - Searching non-linear parameter spaces to find the best-fit model.
 
-`Tutorial 4: Complex Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb>`_
+`Tutorial 4: Complex Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb>`_
 - Composing and fitting more complex models in a scalable and extensible way.
 
-`Tutorial 5: Results and Samples <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb>`_
+`Tutorial 5: Results and Samples <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb>`_
 - Interpreting model-fit results and using the samples for scientific analysis.

notebooks/chapter_3_graphical_models/README.rst

@@ -9,17 +9,17 @@ Files
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Individual Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_1_individual_models.ipynb>`_
+`Tutorial 1: Individual Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_1_individual_models.ipynb>`_
 - Inferring global parameters from a dataset by fitting the model to each individual dataset one-by-one.
 
-`Tutorial 2: Graphical Model <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_2_graphical_model.ipynb>`_
+`Tutorial 2: Graphical Model <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_2_graphical_model.ipynb>`_
 - Fitting the dataset with a graphical model that fits all datasets simultaneously to infer the global parameters.
 
-`Tutorial 3: Graphical Benefits <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb>`_
+`Tutorial 3: Graphical Benefits <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb>`_
 - Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one.
 
-`Tutorial 4: Hierarchical Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb>`_
+`Tutorial 4: Hierarchical Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb>`_
 - Fitting hierarchical models using the graphical modeling framework.
 
-`Tutorial 5: Expectation Propagation <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb>`_
+`Tutorial 5: Expectation Propagation <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb>`_
 - Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).

scripts/chapter_1_introduction/README.rst

@@ -8,17 +8,17 @@ Files
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_1_models.ipynb>`_
+`Tutorial 1: Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_1_models.ipynb>`_
 - What probabilistic models are and how to compose them using PyAutoFit.
 
-`Tutorial 2: Fitting Data <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_2_fitting_data.ipynb>`_
+`Tutorial 2: Fitting Data <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_2_fitting_data.ipynb>`_
 - Fitting a model with an input set of parameters to data and quantifying the goodness of fit.
 
-`Tutorial 3: Non Linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb>`_
+`Tutorial 3: Non Linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb>`_
 - Searching non-linear parameter spaces to find the best-fit model.
 
-`Tutorial 4: Complex Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb>`_
+`Tutorial 4: Complex Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb>`_
 - Composing and fitting more complex models in a scalable and extensible way.
 
-`Tutorial 5: Results and Samples <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb>`_
+`Tutorial 5: Results and Samples <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb>`_
 - Interpreting model-fit results and using the samples for scientific analysis.

scripts/chapter_3_graphical_models/README.rst

@@ -9,17 +9,17 @@ Files
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Individual Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_1_individual_models.ipynb>`_
+`Tutorial 1: Individual Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_1_individual_models.ipynb>`_
 - Inferring global parameters from a dataset by fitting the model to each individual dataset one-by-one.
 
-`Tutorial 2: Graphical Model <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_2_graphical_model.ipynb>`_
+`Tutorial 2: Graphical Model <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_2_graphical_model.ipynb>`_
 - Fitting the dataset with a graphical model that fits all datasets simultaneously to infer the global parameters.
 
-`Tutorial 3: Graphical Benefits <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb>`_
+`Tutorial 3: Graphical Benefits <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb>`_
 - Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one.
 
-`Tutorial 4: Hierarchical Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb>`_
+`Tutorial 4: Hierarchical Models <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb>`_
 - Fitting hierarchical models using the graphical modeling framework.
 
-`Tutorial 5: Expectation Propagation <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.4.13.6/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb>`_
+`Tutorial 5: Expectation Propagation <https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.1.1/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb>`_
 - Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).