Repository: arviz-devs/arviz

Issue:
Add docs references to plot_posterior docstring
Add links to the related functions and classes to all the places where they are mentioned in the [plot_posterior](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_posterior.html) docstring.

## Sample PR:
You can see https://github.com/arviz-devs/arviz/pull/1816 for the function, `plot_autocorr`. 

## Source File
Source file: https://github.com/arviz-devs/arviz/blob/main/arviz/plots/posteriorplot.py

## How to add links
Add the links in the docstring of doc. You can also check the docstring by going to the `[source]`. 

* Add links to the ArviZ functions and classes using [Cross-referencing with Sphinx](https://docs.readthedocs.io/en/stable/guides/cross-referencing-with-sphinx.html).
* Add links to the xarray functions and classes using [Intersphinx](https://docs.readthedocs.io/en/stable/guides/intersphinx.html).
* Add the rest of the external links using this [syntax](https://sublime-and-sphinx-guide.readthedocs.io/en/latest/references.html#links-to-external-web-pages).

## `backend_kwargs` and `kwargs`
For adding `backend_kwargs` as they have been added in #1816, see [Adding backend_kwargs guide](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#adding-backend_kwargs). Follow the same process for `kwargs`. 

## See also section
Add the following functions in the "See also" section:
1. [plot_dist](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_dist.html)
2. [plot_density](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_density.html)
3. [plot_forest](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_forest.html)

See [Adding "see also" section](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#adding-see-also-section) for more details. 

## Checking the preview of docs
For checking the preview of docs, you don't need t set up the project on your local machine. Just follow the [checking the preview guide](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#how-to-check-the-preview-of-docs). 


Discussion:

Resolving patch:
diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py
--- a/arviz/plots/posteriorplot.py
+++ b/arviz/plots/posteriorplot.py
@@ -42,20 +42,20 @@ def plot_posterior(
     Parameters
     ----------
     data: obj
-        Any object that can be converted to an az.InferenceData object
-        Refer to documentation of az.convert_to_dataset for details
+        Any object that can be converted to an :class:`arviz.InferenceData` object.
+        Refer to the documentation of :func:`arviz.convert_to_dataset` for details
     var_names: list of variable names
-        Variables to be plotted, two variables are required. Prefix the variables by `~`
+        Variables to be plotted, two variables are required. Prefix the variables with ``~``
         when you want to exclude them from the plot.
     filter_vars: {None, "like", "regex"}, optional, default=None
         If `None` (default), interpret var_names as the real variables names. If "like",
         interpret var_names as substrings of the real variables names. If "regex",
         interpret var_names as regular expressions on the real variables names. A la
-        `pandas.filter`.
+        ``pandas.filter``.
     transform: callable
         Function to transform data (defaults to None i.e.the identity function)
     coords: mapping, optional
-        Coordinates of var_names to be plotted. Passed to `Dataset.sel`
+        Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`
     grid : tuple
         Number of rows and columns. Defaults to None, the rows and columns are
         automatically inferred.
@@ -63,7 +63,7 @@ def plot_posterior(
         Figure size. If None it will be defined automatically.
     textsize: float
         Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
-        on figsize.
+        on ``figsize``.
     hdi_prob: float, optional
         Plots highest density interval for chosen percentage of density.
         Use 'hide' to hide the highest density interval. Defaults to 0.94.
@@ -105,11 +105,11 @@ def plot_posterior(
         and a circular KDE is used. Only valid for 1D KDE. Defaults to False.
         Only works if `kind == kde`.
     bins: integer or sequence or 'auto', optional
-        Controls the number of bins, accepts the same keywords `matplotlib.hist()` does. Only works
-        if `kind == hist`. If None (default) it will use `auto` for continuous variables and
-        `range(xmin, xmax + 1)` for discrete variables.
+        Controls the number of bins,accepts the same keywords :func:`matplotlib.pyplot.hist` does.
+        Only works if `kind == hist`. If None (default) it will use `auto` for continuous variables
+        and `range(xmin, xmax + 1)` for discrete variables.
     labeller : labeller instance, optional
-        Class providing the method `make_label_vert` to generate the labels in the plot titles.
+        Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
         Read the :ref:`label_guide` for more details and usage examples.
     ax: numpy array-like of matplotlib axes or bokeh figures, optional
         A 2D array of locations into which to plot the densities. If not supplied, Arviz will create
@@ -117,17 +117,24 @@ def plot_posterior(
     backend: str, optional
         Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
     backend_kwargs: bool, optional
-        These are kwargs specific to the backend being used. For additional documentation
-        check the plotting method of the backend.
+        These are kwargs specific to the backend being used, passed to
+        :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`
     show: bool, optional
         Call backend show function.
     **kwargs
-        Passed as-is to plt.hist() or plt.plot() function depending on the value of `kind`.
+        Passed as-is to :func:`matplotlib.pyplot.hist` or :func:`matplotlib.pyplot.plot` function
+        depending on the value of `kind`.
 
     Returns
     -------
     axes: matplotlib axes or bokeh figures
 
+    See Also
+    --------
+    plot_dist : Plot distribution as histogram or kernel density estimates.
+    plot_density : Generate KDE plots for continuous variables and histograms for discrete ones.
+    plot_forest : Forest plot to compare HDI intervals from a number of distributions.
+
     Examples
     --------
     Show a default kernel density plot following style of John Kruschke


Test patch: