{ "item_title" : "Bayesian Missing Data Problems", "item_author" : [" Ming T. Tan", "Guo-Liang Tian", "Kai Wang Ng "], "item_description" : "This book presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors, based on the inverse Bayes formulae. The authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. They describe Monte Carlo simulation, numerical techniques, and optimization methods. The book illustrates the methods with biostatistical models and real-world applications, including mixed effects and hierarchical models, nonresponse and contingency tables, and the constrained parameter problem reformulated as a missing data problem.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/0/36/738/530/0367385309_b.jpg", "price_data" : { "retail_price" : "89.99", "online_price" : "89.99", "our_price" : "89.99", "club_price" : "89.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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