Quantum Bayesian Networks for Ticket Class Probability Analysis

Traditional probabilistic models often struggle to capture the complex, overlapping decision boundaries found in real-world predictive datasets. This course introduces you to quantum Bayesian networks, a cutting-edge approach that applies quantum probability principles to classical prediction tasks, such as analyzing passenger survival and ticket class distributions. By shifting from classical logic to quantum-inspired inference, you will gain a deeper, more nuanced understanding of how variables interact in predictive modeling. You will start with the fundamental concepts of quantum probability, learning how superposition and interference differ from classical probability theory, before moving on to practical calculations. Through step-by-step written explanations and code examples, you will learn to structure networks, calculate quantum-inspired marginal and conditional probabilities, and apply these techniques to classification problems. What you'll learn: - Understand the core differences between classical and quantum probability frameworks - Define the structure and nodes of a quantum Bayesian network for prediction tasks - Calculate marginal and conditional probabilities using quantum interference principles - Model complex dependencies between ticket classes, demographics, and survival outcomes - Apply modern Python-based quantum simulation libraries to set up and query your network - Evaluate the accuracy of your quantum predictive models against classical baselines This course is structured to take you from foundational quantum probability theory to hands-on model implementation. You will explore structured written lessons that explain the underlying mathematics, followed by guided programming exercises to build your own network from scratch. This course is designed for data analysts, programmers, and predictive modelers who want to explore quantum-inspired machine learning. No prior background in quantum computing is required, though a basic understanding of Python and standard probability is helpful.