[no ci] website: list example notebooks only on Examples page

* one place less to update
* fix one link to a notebook

Author: vpratz

docsrc/source/index.md

@@ -45,15 +45,7 @@ history = workflow.fit_online(epochs=50, batch_size=32, num_batches_per_epoch=50
 diagnostics = workflow.plot_default_diagnostics(test_data=300)
 ```
 
-For an in-depth exposition, check out our walkthrough notebooks below.
-
-1. [Linear regression starter example](_examples/Linear_Regression_Starter.ipynb)
-2. [From ABC to BayesFlow](_examples/From_ABC_to_BayesFlow.ipynb)
-3. [Two moons starter example](_examples/Two_Moons_Starter.ipynb)
-4. [Rapid iteration with point estimators](_examples/Lotka_Volterra_point_estimation_and_expert_stats.ipynb)
-5. [SIR model with custom summary network](_examples/SIR_Posterior_Estimation.ipynb)
-6. [Bayesian experimental design](_examples/Bayesian_Experimental_Design.ipynb)
-7. [Simple model comparison example](_examples/One_Sample_TTest.ipynb)
+For an in-depth exposition, check out our walkthrough notebooks in the {doc}`Examples <../examples>` section.
 
 More tutorials are always welcome! Please consider making a pull request if you have a cool application that you want to contribute.
 

examples/SIR_Posterior_Estimation.ipynb

@@ -84,7 +84,7 @@
    "id": "39846c15b88eaf8e",
    "metadata": {},
    "source": [
-    "As described in our [very first notebook](linear_Regression_Starter.ipynb), a generative model consists of a prior (encoding suitable parameter ranges) and a simulator (generating data given simulations). Our underlying model distinguishes between susceptible, $S$, infected, $I$, and recovered, $R$, individuals with infection and recovery occurring at a constant transmission rate $\\lambda$ and constant recovery rate $\\mu$, respectively. The model dynamics are governed by the following system of ODEs:\n",
+    "As described in our [very first notebook](Linear_Regression_Starter.ipynb), a generative model consists of a prior (encoding suitable parameter ranges) and a simulator (generating data given simulations). Our underlying model distinguishes between susceptible, $S$, infected, $I$, and recovered, $R$, individuals with infection and recovery occurring at a constant transmission rate $\\lambda$ and constant recovery rate $\\mu$, respectively. The model dynamics are governed by the following system of ODEs:\n",
     "\n",
     "$$\n",
     "\\begin{align}\n",