Hands-on computational modeling techniques for Bayesian analysis enables practical learning
Utilizes R and Stan for accessible and powerful statistical modeling
Causal inference frameworks help users address causal questions in data analysis
Multilevel and hierarchical models provide tools for complex data structures
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The premier modern introduction to applied Bayesian data analysis. Engineered for statisticians, data scientists, researchers, and graduate students, this acclaimed 2nd Edition provides hands-on computational modeling techniques utilizing R and Stan, causal inference frameworks, and multilevel hierarchical models.