{"product_id":"dependence-models-via-hierarchical-structures-hardback-9781009584111","title":"Dependence Models via Hierarchical Structures (Hardback) 9781009584111","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDependence Models via Hierarchical Structures\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eLearn to construct your own dependence models with this step-by-step look at models in a Bayesian analysis context.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eLuis E. Nieto-Barajas (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781009584111, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 March 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e149 pages\u003cbr\u003e23.2 x 15.5 x 1.5 cm, 0.36 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'This monograph will be of interest to graduate or advanced undergraduate students that are looking for principled and elegant ways to construct models with stochastic dependence. The text goes from the simplest scenario (exchangeability) to more complicated spatio-temporal models. Many references and concrete examples are introduced and discussed in detail.' Fernando A. Quintana, Pontificia Universidad Católica de Chile\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eBringing together years of research into one useful resource, this text empowers the reader to creatively construct their own dependence models. Intended for senior undergraduate and postgraduate students, it takes a step-by-step look at the construction of specific dependence models, including exchangeable, Markov, moving average and, in general, spatio-temporal models. All constructions maintain a desired property of pre-specifying the marginal distribution and keeping it invariant. They do not separate the dependence from the marginals and the mechanisms followed to induce dependence are so general that they can be applied to a very large class of parametric distributions. All the constructions are based on appropriate definitions of three building blocks: prior distribution, likelihood function and posterior distribution, in a Bayesian analysis context. All results are illustrated with examples and graphical representations. Applications with data and code are interspersed throughout the book, covering fields including insurance and epidemiology.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e 2. Conjugate models\u003cbr\u003e 3. Exchangeable sequences\u003cbr\u003e 4. Markov sequences\u003cbr\u003e 5. General dependent sequences\u003cbr\u003e 6. Temporal dependent sequences\u003cbr\u003e 7. Spatial dependent sequences\u003cbr\u003e 8. Multivariate dependent sequences\u003cbr\u003e Appendix. Data sets\u003cbr\u003e References\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Probability \u0026amp; statistics [\u003ca title=\"See our other books on Probability \u0026amp; statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Probability%20\u0026amp;%20statistics%20%5BPBT%5D%22\"\u003ePBT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Brand New","offer_id":52501136408856,"sku":"9781009584111","price":45.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781009584111i.jpg?v=1786211875","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/dependence-models-via-hierarchical-structures-hardback-9781009584111","provider":"Freshly Printed Books","version":"1.0","type":"link"}