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Explore the fundamental concepts of Bayesian statistical inference through the metaphor of forking paths in this lecture that introduces how different possible worlds branch out based on observed data. Learn to think about probability as a measure of uncertainty rather than frequency, understand how prior beliefs combine with evidence to update knowledge, and discover how the "garden of forking data" framework helps visualize the logical structure underlying Bayesian reasoning. Master the conceptual foundations that will support more advanced statistical modeling techniques throughout the course, with practical examples demonstrating how this philosophical approach translates into concrete analytical methods.
