📅 2024-12-05 — Session: Enhanced Data Visualization and Game Theory Exploration
🕒 14:35–16:15
🏷️ Labels: Data Visualization, Game Theory, Python, Bayesian Inference
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization techniques using Python libraries and explore foundational and advanced concepts in game theory.
Key Activities
- Updated Contour Plot: Implemented a double-line effect in contour plots using Matplotlib to improve visual clarity.
- Enhanced Seaborn Plot: Improved plot aesthetics by modifying color palettes, grid lines, legend styling, and typography.
- Game Theory Exploration: Explored two-player game theory scenarios focusing on expected payoffs and optimization strategies.
- Probabilistic Modeling: Developed functions for probabilistic modeling in game theory to calculate expected payoffs.
- Bayesian Inference Resources: Compiled resources for Bayesian inference and game theory applications in investing.
Achievements
- Successfully enhanced data visualization techniques with Matplotlib and Seaborn.
- Developed a deeper understanding of game theory concepts, including Bayesian Nash Equilibrium.
- Compiled a comprehensive list of resources for further exploration of Bayesian inference in finance.
Pending Tasks
- Further exploration of Bayesian games in multi-player scenarios.
- Application of enhanced visualization techniques in other data analysis projects.