v1.0.2

README.md

@@ -47,3 +47,11 @@ A useful statistic when working with Entropy Pooling is the effective number of
 scenarios introduced by [Meucci (2012)](https://ssrn.com/abstract=1971808). For
 a causal Bayesian nets overlay on top of Entropy Pooling, see
 [Vorobets (2023)](https://ssrn.com/abstract=4444291).
+
+Video walkthroughs
+------------------
+
+Video walkthroughs of the two notebook examples for this package are available here
+https://youtu.be/hDt103zEML8 and here https://youtu.be/DK1Pv5tuLgo. The videos give
+additional insights into the Entropy Pooling theory and its sequential refinements.
+It is highly encouraged to watch these two quickly increase your understanding.

environment.yml

@@ -4,7 +4,10 @@ channels:
 dependencies:
   - python=3.10
   - scipy
+  - seaborn
+  - pandas
   - pip
   - pip:
       - fortitudo.tech
-      - entropy-pooling
+      - entropy-pooling
+      - yfinance

example/EntropyPooling.ipynb → example/1_EntropyPooling.ipynb

@@ -7,7 +7,9 @@
     "# Entropy Pooling example\n",
     "This notebook replicates the results of the original EP method (Table 4 and Table 7) from the Sequential Entropy Pooling Heuristics article, available on https://ssrn.com/abstract=3936392.\n",
     "\n",
-    "You can watch a video walkthrough of the example here: https://youtu.be/hDt103zEML8."
+    "You can watch a video walkthrough of the example here: https://youtu.be/hDt103zEML8.\n",
+    "\n",
+    "For additional useful information about Entropy Pooling and an application of the sequential heuristic H1 to S&P 500 and STOXX 50 data, see the next example as well as this video walkthrough: https://youtu.be/DK1Pv5tuLgo."
    ]
   },
   {
@@ -795,7 +797,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.12.0"
   },
   "orig_nbformat": 4,
   "vscode": {