Setting CPD Values in Bayesian Networks with Python

Bayesian networks are powerful tools for reasoning under uncertainty, but their accuracy depends entirely on how well you define their underlying probabilities. This course provides a clear, step-by-step path to understanding, calculating, and setting Conditional Probability Distributions (CPDs) to build reliable predictive models. You will transition from theoretical probability concepts to hands-on configuration using modern Python tools. Learn to structure your network, define dependencies, and update CPDs based on real-world data. What you will learn: Configure foundational CPDs for discrete and continuous variables; Apply modern Python libraries like pgmpy to construct network structures; Estimate probabilities from observational data using maximum likelihood estimation; Implement Laplace smoothing to handle missing or rare scenarios; Validate network inference and run diagnostic queries to test model accuracy. We begin with the core mathematical definitions of conditional probability before moving into practical coding workflows and network validation. This course is designed for data analysts, software developers, and aspiring data scientists who are new to probabilistic graphical models and want a practical, text-based guide. Start building smarter, uncertainty-aware models today.