{"title":"Peter M. Lee","description":"\u003cp\u003eDelve into the world of statistics with the insightful works of Peter M. Lee. Known for his authoritative approach in the realm of \u003cem\u003eBayesian Statistics\u003c\/em\u003e, Lee offers readers a comprehensive understanding of this complex yet fascinating subject.\u003c\/p\u003e\n\n\u003cp\u003eLee's books are perfect for those who wish to explore the meticulous field of scientific and natural statistical methods. His clear explanations and practical approach make these books a valuable resource for both beginners and advanced learners in the field. Through his work, Peter M. Lee demystifies statistics, making it accessible and engaging for all readers.\u003c\/p\u003e\n\n\u003cp\u003eWhether you're a student, educator, or just someone with an interest in the world of science and nature, Peter M. Lee's collection offers essential insights into the principles and applications of Bayesian statistics. Explore his collection today and expand your knowledge with one of the leading experts in the field.\u003c\/p\u003e","products":[{"product_id":"bayesian-statistics-by-peter-m-lee-9781118332573","title":"Bayesian Statistics","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003e\u003ci\u003eBayesian Statistics\u003c\/i\u003e is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.\u003c\/p\u003e\n\n\u003cp\u003eThis new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation, and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples.\u003c\/p\u003e\n\n\u003cp\u003e\u003ci\u003eThis edition:\u003c\/i\u003e\u003c\/p\u003e \n\u003cul\u003e\n  \u003cli\u003eIncludes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS, and R2OpenBUGS.\u003c\/li\u003e\n  \u003cli\u003ePresents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC), and Reversible Jump Markov Chain Monte Carlo (RJMCMC).\u003c\/li\u003e\n  \u003cli\u003eProvides extensive examples throughout the book to complement the theory presented.\u003c\/li\u003e\n  \u003cli\u003eAccompanied by a supporting website featuring new material and solutions.\u003c\/li\u003e\n\u003c\/ul\u003e \n\n\u003cp\u003eMore and more students are realising that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":46854032851180,"sku":"9781118332573","price":107.99,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/4272793482586.jpg?v=1759232059"}],"url":"https:\/\/bookhero.co.nz\/collections\/peter-m-lee.oembed","provider":"Book Hero","version":"1.0","type":"link"}