Release 2026.5.8.2: bump Colab URL tag refs

Author: rhayes777

notebooks/chapter_1_introduction/README.md

@@ -7,12 +7,12 @@ a non-linear search and inspect and interpret the results.
 
 The chapter contains the following tutorials:
 
-- [Tutorial 1: Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_1_models.ipynb) — What probabilistic models are and how to compose them using PyAutoFit.
+- [Tutorial 1: Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/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 2: Fitting Data](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb) — Searching non-linear parameter spaces to find the best-fit model.
+- [Tutorial 3: Non Linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb) — Composing and fitting more complex models in a scalable and extensible way.
+- [Tutorial 4: Complex Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb) — Interpreting model-fit results and using the samples for scientific analysis.
+- [Tutorial 5: Results and Samples](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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.md

@@ -8,12 +8,12 @@ and 'global' parameters that fit for global trends across the whole dataset.
 
 The chapter contains the following tutorials:
 
-- [Tutorial 1: Individual Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.1/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 1: Individual Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/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 2: Graphical Model](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb) — Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one.
+- [Tutorial 3: Graphical Benefits](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework.
+- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).
+- [Tutorial 5: Expectation Propagation](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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.md

@@ -7,12 +7,12 @@ a non-linear search and inspect and interpret the results.
 
 The chapter contains the following tutorials:
 
-- [Tutorial 1: Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_1_models.ipynb) — What probabilistic models are and how to compose them using PyAutoFit.
+- [Tutorial 1: Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/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 2: Fitting Data](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb) — Searching non-linear parameter spaces to find the best-fit model.
+- [Tutorial 3: Non Linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb) — Composing and fitting more complex models in a scalable and extensible way.
+- [Tutorial 4: Complex Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb) — Interpreting model-fit results and using the samples for scientific analysis.
+- [Tutorial 5: Results and Samples](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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.md

@@ -8,12 +8,12 @@ and 'global' parameters that fit for global trends across the whole dataset.
 
 The chapter contains the following tutorials:
 
-- [Tutorial 1: Individual Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.1/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 1: Individual Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/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 2: Graphical Model](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb) — Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one.
+- [Tutorial 3: Graphical Benefits](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework.
+- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/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/2026.5.8.1/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).
+- [Tutorial 5: Expectation Propagation](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.8.2/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).