{"product_id":"bayesian-reasoning-in-data-analysis-a-critical-introduction-paperback","title":"Bayesian Reasoning in Data Analysis: A Critical Introduction - Paperback","description":"\u003cp\u003eby \u003cb\u003eGiulio D'Agostini\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eThis book provides a multi-level introduction to Bayesian reasoning (as opposed to \"conventional statistics\") and its applications to data analysis. The basic ideas of this \"new\" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper\/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide -- under well-defined assumptions  -- with \"standard\" methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003eThis book provides a multi-level introduction to Bayesian reasoning (as opposed to \"conventional statistics\") and its applications to data analysis. The basic ideas of this \"new\" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper\/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide under well-defined assumptions! with \"standard\" methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 352\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.8 x 8.9 x 6 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 16, 2003\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47416218452146,"sku":"9789814447959","price":142.56,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/946df2aa259637d96aa0220303947554.webp?v=1778400618","url":"https:\/\/box.dadyminds.org\/products\/bayesian-reasoning-in-data-analysis-a-critical-introduction-paperback","provider":"DADYMINDS BOX","version":"1.0","type":"link"}