{ "item_title" : "Markov Chain Monte Carlo", "item_author" : [" Dani Gamerman", "Hedibert Freita Lopes", "Hedibert F. Lopes "], "item_description" : "Incorporating changes in theory and highlighting new applications, this book presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. This second edition includes many new examples in the chapters on Gibbs sampling and Metropolis-Hastings algorithms. It incorporates all the recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection. It also features many worked examples and discusses computation using both R and WinBUGS. With additional exercises and selected solutions within the text, it offers all data sets and software for download from the Web.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/58/488/587/1584885874_b.jpg", "price_data" : { "retail_price" : "166.99", "online_price" : "166.99", "our_price" : "166.99", "club_price" : "166.99", "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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