Quantum Bayesian Networks for Relative Survival Probabilities

How do family connections affect survival rates in complex historical events? Traditional probability models often struggle to capture the subtle, non-linear dependencies of human relationships during crises. This written course guides you through modeling familial relationships and survival probabilities using a quantum Bayesian network approach, using historical passenger data to ground your calculations. You will start by mastering the foundational concepts of quantum probability, decision theory, and convenience functions before applying them to structured relationship data. Through clear text explanations and step-by-step mathematical walkthroughs, you will learn how to structure complex conditional dependencies and interpret quantum-inspired decision models. What you will learn: Understand the core principles of quantum probability and how they differ from classical Bayesian networks; Configure convenience functions to represent familial relationships and decision-making priorities; Analyze historical passenger datasets to extract structured relationship features; Build a quantum Bayesian network structure to model survival outcomes; Practice calculating joint and conditional probabilities with quantum-inspired interference terms; Interpret model predictions to discover how family size and connection types impact survival rates. We begin with essential terminology and the mathematical foundations of quantum cognition, then progress to structuring relationship data and building the predictive network. This course is designed for data analysts, programmers, and probability enthusiasts who want to explore quantum-inspired modeling; no prior background in quantum physics is required. Start reading today to expand your predictive modeling toolkit with quantum Bayesian techniques.