{"product_id":"modeling-and-reasoning-with-bayesian-networks-paperback-9781107678422","title":"Modeling and Reasoning with Bayesian Networks (Paperback \/ softback) 9781107678422","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eModeling and Reasoning with Bayesian Networks\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eThis book introduces the formal foundations and practical applications of Bayesian networks.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAdnan Darwiche (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781107678422, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 7 August 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e562 pages, 246 b\/w illus.  64 tables  342 exercises\u003cbr\u003e25.4 x 17.8 x 2.9 cm, 0.96 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] book will make an excellent textbook; it covers topics suitable for both undergraduate and graduate courses. It will also help practitioners get a firm grasp of the fundamentals of modeling and inference with BNs, as well as some recent advances.' ACM Computing Reviews\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e 2. Propositional logic\u003cbr\u003e 3. Probability calculus\u003cbr\u003e 4. Bayesian networks\u003cbr\u003e 5. Building Bayesian networks\u003cbr\u003e 6. Inference by variable elimination\u003cbr\u003e 7. Inference by factor elimination\u003cbr\u003e 8. Inference by conditioning\u003cbr\u003e 9. Models for graph decomposition\u003cbr\u003e 10. Most likely instantiations\u003cbr\u003e 11. The complexity of probabilistic inference\u003cbr\u003e 12. Compiling Bayesian networks\u003cbr\u003e 13. Inference with local structure\u003cbr\u003e 14. Approximate inference by belief propagation\u003cbr\u003e 15. Approximate inference by stochastic sampling\u003cbr\u003e 16. Sensitivity analysis\u003cbr\u003e 17. Learning: the maximum likelihood approach\u003cbr\u003e 18. Learning: the Bayesian approach\u003cbr\u003e Appendix A: notation\u003cbr\u003e Appendix B: concepts from information theory\u003cbr\u003e Appendix C: fixed point iterative methods\u003cbr\u003e Appendix D: constrained optimization.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematical theory of computation [\u003ca title=\"See our other books on Mathematical theory of computation\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematical%20theory%20of%20computation%20%5BUYA%5D%22\"\u003eUYA\u003c\/a\u003e], 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":"Default Title","offer_id":46003304825112,"sku":"9781107678422","price":53.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781107678422i_c8a900ef-fbb8-4986-afa6-a0165445e66a.jpg?v=1694507545","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/modeling-and-reasoning-with-bayesian-networks-paperback-9781107678422","provider":"Freshly Printed Books","version":"1.0","type":"link"}