Biased Coin Toss

This project is a hands-on exploration of a classic probability scenario: analyzing a biased coin flip. Beyond its simplicity, this exercise demonstrates key statistical tools that build a foundation for more advanced quantitative analysis and decision-making under uncertainty.

📂 Files

• Biased Coin Toss.ipynb — Interactive Jupyter Notebook with complete code, explanations, and visualizations.
• README.md — This overview.

📊 Concepts Covered

Through this notebook, I practice and demonstrate:

✅ Simulating biased coin flips
✅ Applying Bayesian inference to update beliefs in real-time
✅ Constructing credible intervals
✅ Using Monte Carlo simulation for uncertainty estimation
✅ Implementing sequential analysis for evidence-based stopping rules

These techniques reflect core skills in probabilistic modeling, which are directly relevant in areas such as quantitative trading, algorithmic decision systems, and real-time risk management.

⚙️ Requirements

• Python
• Jupyter Notebook
• Python libraries:
  • numpy
  • scipy
  • matplotlib
  • seaborn

Install dependencies easily:

pip install numpy scipy matplotlib seaborn