Add reference to SBC interpretation guide in starter examples (#236)

Author: elseml

examples/Linear_Regression.ipynb

@@ -1006,7 +1006,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The histograms don't look ideal yet but also not too bad. Inference on the intercept $\\beta_0$ is a bit underconfident. That is, the approximate posteriors are a bit wider than the corresponding correct posteriors. The SBC histograms have some drawbacks on how the confidence bands are computed, so we recommend using another kind of plot that is based on the empirical cumulative distribution function (ECDF). For the ECDF, we can compute better confidence bands than for histograms, to the SBC ECDF plot is usually preferable."
+    "The histograms don't look ideal yet but also not too bad. Inference on the intercept $\\beta_0$ is a bit underconfident. That is, the approximate posteriors are a bit wider than the corresponding correct posteriors. The SBC histograms have some drawbacks on how the confidence bands are computed, so we recommend using another kind of plot that is based on the empirical cumulative distribution function (ECDF). For the ECDF, we can compute better confidence bands than for histograms, so the SBC ECDF plot is usually preferable. [This SBC interpretation guide by Martin Modrák](https://hyunjimoon.github.io/SBC/articles/rank_visualizations.html) gives further background information and also practical examples of how to interpret the SBC plots."
    ]
   },
   {

examples/Quickstart_Amortized_Posterior_Estimation.ipynb

@@ -947,7 +947,8 @@
     "The posterior looks as we have expected in this case. However, in general, we do not know how the posterior is supposed to look like for any specific dataset. As such, we need diagnostics that validate the correctness of the inferred posterior. One such diagnostic is simulation-based calibration(SBC), which we can apply for free due to amortization. For more details on SBC and diagnostic plots, see:\n",
     "\n",
     "1. Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. (2018). Validating Bayesian inference algorithms with simulation-based calibration. *arXiv preprint*.\n",
-    "2. Säilynoja, T., Bürkner, P. C., & Vehtari, A. (2022). Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. *Statistics and Computing*."
+    "2. Säilynoja, T., Bürkner, P. C., & Vehtari, A. (2022). Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. *Statistics and Computing*.\n",
+    "3. The practical SBC interpretation guide by Martin Modrák: https://hyunjimoon.github.io/SBC/articles/rank_visualizations.html"
    ]
   },
   {
@@ -963,7 +964,7 @@
  ],
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-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "bf2",
    "language": "python",
    "name": "python3"
   },
@@ -977,7 +978,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,