Update doc/example/distributions.ipynb

dweindl · m-philipps · web-flow · commit a5d2a3d6f560 · 2025-02-11T12:12:03.000+01:00
Co-authored-by: Maren Philipps &lt;55318391+m-philipps@users.noreply.github.com&gt;
diff --git a/doc/example/distributions.ipynb b/doc/example/distributions.ipynb
@@ -165,7 +165,7 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "Prior distributions can also be defined on the scaled parameters (i.e., transformed according to `parameterScale`) by using the types `parameterScaleUniform`, `parameterScaleNormal` or `parameterScaleLaplace`. In these cases, the distribution parameter are interpreted on the transformed parameter scale (but not the parameter bounds, see below). This implies, that for `parameterScale=lin`, there is no difference between `parameterScaleUniform` and `uniform`.",
+   "source": "Prior distributions can also be defined on the scaled parameters (i.e., transformed according to `parameterScale`) by using the types `parameterScaleUniform`, `parameterScaleNormal` or `parameterScaleLaplace`. In these cases, the distribution parameters are interpreted on the transformed parameter scale (but not the parameter bounds, see below). This implies, that for `parameterScale=lin`, there is no difference between `parameterScaleUniform` and `uniform`.",
    "id": "263c9fd31156a4d5"
   },
   {

Original file line number	Diff line number	Diff line change
`@@ -165,7 +165,7 @@`
`165`	`165`	`{`
`166`	`166`	`"metadata": {},`
`167`	`167`	`"cell_type": "markdown",`
`168`		- "source": "Prior distributions can also be defined on the scaled parameters (i.e., transformed according to `parameterScale`) by using the types `parameterScaleUniform`, `parameterScaleNormal` or `parameterScaleLaplace`. In these cases, the distribution parameter are interpreted on the transformed parameter scale (but not the parameter bounds, see below). This implies, that for `parameterScale=lin`, there is no difference between `parameterScaleUniform` and `uniform`.",
	`168`	+ "source": "Prior distributions can also be defined on the scaled parameters (i.e., transformed according to `parameterScale`) by using the types `parameterScaleUniform`, `parameterScaleNormal` or `parameterScaleLaplace`. In these cases, the distribution parameters are interpreted on the transformed parameter scale (but not the parameter bounds, see below). This implies, that for `parameterScale=lin`, there is no difference between `parameterScaleUniform` and `uniform`.",
`169`	`169`	`"id": "263c9fd31156a4d5"`
`170`	`170`	`},`
`171`	`171`	`{`