Bring stochastic volatility notebook in line with style guide

Fixing dashes in reference entry

Removing extra lines from reference bib file

Author: ckrapu

examples/case_studies/stochastic_volatility.ipynb

@@ -148,6 +148,16 @@ @book{hayes2017introduction
   year          = {2017},
   publisher     = {Guilford publications}
 }
+@article{hoffman2014nuts,
+  title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
+  author        = {Hoffman, Matthew and Gelman, Andrew},
+  year          = {2014},
+  journal       = {Journal of Machine Learning Research},
+  volume        = {15},
+  issue         = {1},
+  pages         = {1593--1623},
+  url           = {https://dl.acm.org/doi/10.5555/2627435.2638586}
+}
 @misc{hogg2010data,
   title         = {Data analysis recipes: Fitting a model to data},
   author        = {David W. Hogg and Jo Bovy and Dustin Lang},

examples/references.bib

@@ -6,13 +6,20 @@ jupytext:
     format_version: 0.13
     jupytext_version: 1.13.7
 kernelspec:
-  display_name: Python 3 (ipykernel)
+  display_name: pymc_env
   language: python
-  name: python3
+  name: pymc_env
 ---
 
+(notebook_name)=
 # Stochastic Volatility model
 
+:::{post} June 17, 2022
+:tags: time series
+:category: basic
+:author: 
+:::
+
 ```{code-cell} ipython3
 import os
 
@@ -26,15 +33,15 @@ rng = np.random.RandomState(1234)
 az.style.use("arviz-darkgrid")
 ```
 
-Asset prices have time-varying volatility (variance of day over day `returns`). In some periods, returns are highly variable, while in others very stable. Stochastic volatility models model this with a latent volatility variable, modeled as a stochastic process. The following model is similar to the one described in the No-U-Turn Sampler paper, Hoffman (2011) p21.
+Asset prices have time-varying volatility (variance of day over day `returns`). In some periods, returns are highly variable, while in others very stable. Stochastic volatility models model this with a latent volatility variable, modeled as a stochastic process. The following model is similar to the one described in the No-U-Turn Sampler paper, {cite:p}`hoffman2014nuts`.
 
 $$ \sigma \sim Exponential(50) $$
 
 $$ \nu \sim Exponential(.1) $$
 
 $$ s_i \sim Normal(s_{i-1}, \sigma^{-2}) $$
 
-$$ log(r_i) \sim t(\nu, 0, exp(-2 s_i)) $$
+$$ \log(r_i) \sim t(\nu, 0, \exp(-2 s_i)) $$
 
 Here, $r$ is the daily return series and $s$ is the latent log volatility process.
 
@@ -182,9 +189,24 @@ axes[1].set_title("Posterior volatility");
 
 ## References
 
-1. Hoffman & Gelman. (2011). [The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo](http://arxiv.org/abs/1111.4246).
+:::{bibliography}
+:filter: docname in docnames
+:::
 
 ```{code-cell} ipython3
 %load_ext watermark
 %watermark -n -u -v -iv -w -p aesara,xarray
 ```
+
+## Authors
+
++++
+
+* Updated by Abhipsha Das on July 24, 2021 ([pymc-examples#155](https://github.com/pymc-devs/pymc-examples/pull/155))
+* Updated by Michael Osthege on June 1, 2022 ([pymc-examples#343](https://github.com/pymc-devs/pymc-examples/pull/343))
+* Updated by Christopher Krapu on June 17, 2022 ([pymc-examples#XX](https://github.com/pymc-devs/pymc-examples/pull/XX))
+
++++
+
+:::{include} ../page_footer.md
+:::