Log-Likelihood in Bayesian Networks with Missing Data

Working with real-world data often means encountering incomplete datasets, posing a significant challenge for model evaluation. This course provides a foundational understanding of how to build and evaluate probabilistic models even when information is missing. By the end of this course, you will be equipped to understand and apply methods for calculating log-likelihood in Bayesian networks, confidently handling scenarios with missing data to build more robust models. This course will teach you to: * Understand the fundamental principles of probability and Bayesian inference. * Learn to construct and interpret the structure of Bayesian networks. * Calculate log-likelihood scores to evaluate the fit of Bayesian network models. * Identify and categorize different types of missing data mechanisms. * Apply the Expectation-Maximization (EM) algorithm for parameter estimation with incomplete data. * Analyze the implications of ignoring or improperly handling missing data in model evaluation. * Practice evaluating model performance and making informed decisions based on log-likelihood. The course begins with core concepts of probability and Bayesian networks, gradually progressing to the definition and calculation of log-likelihood. It then delves into practical strategies for addressing missing data, including the powerful EM algorithm, to ensure accurate model evaluation. This course is designed for absolute beginners with no prior experience in Bayesian networks or advanced statistics. All necessary concepts are introduced from scratch. Start your journey to building more reliable probabilistic models with incomplete data today.