{"product_id":"bayesian-models-a-statistical-primer-for-ecologists-9780691159287","title":"Bayesian Models: A Statistical Primer for Ecologists","description":"\u003cp\u003eBayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ci\u003eBayesian Models\u003c\/i\u003e is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. \u003cp\u003e\u003c\/p\u003eThis primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.\u003cbr\u003e\u003cul\u003e\n\u003cli\u003ePresents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians\u003c\/li\u003e\n\u003cli\u003eCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more\u003c\/li\u003e\n\u003cli\u003eDeemphasizes computer coding in favor of basic principles\u003c\/li\u003e\n\u003cli\u003eExplains how to write out properly factored statistical expressions representing Bayesian models\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eN. Thompson Hobbs\u003c\/b\u003e is senior research scientist at the Natural Resource Ecology Laboratory and professor in the Department of Ecosystem Science and Sustainability at Colorado State University. \u003cb\u003eMevin B. Hooten\u003c\/b\u003e is associate professor in the Department of Fish, Wildlife, and Conservation Biology and the Department of Statistics at Colorado State University, and assistant unit leader in the US Geological Survey's Colorado Cooperative Fish and Wildlife Research Unit.\u003cbr\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":50503681081618,"sku":"9780691159287","price":65.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_14a0f553-d53c-4274-b290-f3f64f3a10ee.jpg?v=1730795526","url":"https:\/\/surprise-castle.myshopify.com\/products\/bayesian-models-a-statistical-primer-for-ecologists-9780691159287","provider":"Surprise Castle","version":"1.0","type":"link"}