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Explore Bayesian statistical methods and computational approaches through this comprehensive lecture series spanning 20 sessions. Master the fundamentals of statistical rethinking by moving beyond traditional frequentist approaches to embrace Bayesian inference, multilevel modeling, and modern computational techniques. Learn to build and interpret statistical models using probabilistic thinking, understand the philosophy behind Bayesian statistics, and apply these methods to real-world data analysis problems. Develop skills in model comparison, causal inference, and handling uncertainty in statistical analysis while working with practical examples and case studies throughout the curriculum.
