Have pooling model images be local as they keep disappearing.

examples/generalized_linear_models/GLM-hierarchical.ipynb

@@ -233,7 +233,7 @@
     "\n",
     "Where $i$ represents the measurement, $c$ the county and floor contains a 0 or 1 if the house has a basement or not, respectively. If you need a refresher on Linear Regressions in `PyMC`, check out my [previous blog post](http://twiecki.github.io/blog/2013/08/12/bayesian-glms-1/). Critically, we are only estimating *one* intercept and *one* slope for all measurements over all counties pooled together as illustrated in the graphic below ($\\theta$ represents $(\\alpha, \\beta)$ in our case and $y_i$ are the measurements of the $i$th county).\n",
     "\n",
-    "![pooled](http://f.cl.ly/items/0R1W063h1h0W2M2C0S3M/Screen%20Shot%202013-10-10%20at%208.22.21%20AM.png)\n",
+    "<img src=\"pooled_model.png\"/>\n",
     "\n",
     "### Unpooled measurements: separate regressions\n",
     "But what if we are interested in whether different counties actually have different relationships (slope) and different base-rates of radon (intercept)? Then you might say \"OK then, I'll just estimate $n$ (number of counties) different regressions -- one for each county\". In math-speak that model would be:\n",
@@ -242,7 +242,7 @@
     "\n",
     "Note that we added the subindex $c$ so we are estimating $n$ different $\\alpha$s and $\\beta$s -- one for each county.\n",
     "\n",
-    "![unpooled](http://f.cl.ly/items/38020n2t2Y2b1p3t0B0e/Screen%20Shot%202013-10-10%20at%208.23.36%20AM.png)\n",
+    "<img src=\"unpooled_model.png\"/>\n",
     "\n",
     "This is the extreme opposite model; where above we assumed all counties are exactly the same, here we are saying that they share no similarities whatsoever. As we show below, this type of model can be very noisy when we have little data per county, as is the case in this data set.\n",
     "\n",
@@ -255,7 +255,7 @@
     "\n",
     "We thus assume the intercepts $\\alpha$ and slopes $\\beta$ to come from a normal distribution centered around their respective group mean $\\mu$ with a certain standard deviation $\\sigma^2$, the values (or rather posteriors) of which we also estimate. That's why this is called a multilevel, hierarchical or partial-pooling modeling.\n",
     "\n",
-    "![hierarchical](http://f.cl.ly/items/1B3U223i002y3V2W3r0W/Screen%20Shot%202013-10-10%20at%208.25.05%20AM.png)\n",
+    "<img src=\"partial_pooled_model.png\"/>\n",
     "\n",
     "How do we estimate such a complex model you might ask? Well, that's the beauty of Probabilistic Programming -- we just formulate the model we want and press our [Inference Button(TM)](http://twiecki.github.io/blog/2013/08/12/bayesian-glms-1/). \n",
     "\n",
@@ -1883,7 +1883,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1897,7 +1897,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.6"
   },
   "latex_envs": {
    "bibliofile": "biblio.bib",