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Using Bayesian methods to analyze data has become common in applied statistics, social sciences, and medicine, along with other disciplines requiring close work with a diverse set of data. In this undergraduate text, Congdon (Queen Mary College, U. of London) takes a practical and accessible approach, focusing on statistical computing and applied data as he covers the principles of Bayesian inference, model comparison and choice, regression for metric outcomes, models for binary and count outcomes, random effect and latent variable models for multi-category outcomes, ordinal regression, discrete spatial data, time series models for discrete variables, hierarchical and panel data models and missing-data models.
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