{"product_id":"9780198568322","title":"Data Analysis: A Bayesian Tutorial","description":"\u003cp\u003eStatistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.\u003cbr\u003e\u003cbr\u003eThis text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.\u003cbr\u003e\u003cbr\u003eThe Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.\u003c\/p\u003e","brand":"Oxford University Press, USA","offers":[{"title":"Default Title","offer_id":47016497119472,"sku":"9780198568322","price":53.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9780198568322_p0.jpg?v=1763667900","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9780198568322","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}