{"product_id":"bayes-rules-an-introduction-to-applied-bayesian-modeling-alicia-a-johnson-9780367255398","title":"Bayes Rules!: An Introduction to Applied Bayesian Modeling","description":"\u003cp\u003e\u003cstrong\u003eAn Introduction to Applied Bayesian Modeling\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience.\u003cb\u003e\u003c\/b\u003eIt empowers readers to weave Bayesian approaches into their everyday practice. \u003c\/p\u003e\n\u003ch3\u003eOmschrijving\u003c\/h3\u003e\n\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAn engaging, sophisticated, and fun introduction to the field of Bayesian statistics, \u003cstrong\u003eBayes Rules!: An Introduction to Applied Bayesian Modeling\u003c\/strong\u003e brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. the book assumes that readers are familiar with the content covered in a typical undergraduate-level introductory statistics course. Readers will also, ideally, have some experience with undergraduate-level probability, calculus, and the R statistical software. Readers without this background will still be able to follow along so long as they\u003cbr\u003eare eager to pick up these tools on the fly as all R code is provided.Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeatures\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e* Utilizes data-driven examples and exercises.\u003c\/p\u003e\n\u003cp\u003e* Emphasizes the iterative model building and evaluation process.\u003c\/p\u003e\n\u003cp\u003e* Surveys an interconnected range of multivariable regression and classification models.\u003c\/p\u003e\n\u003cp\u003e* Presents fundamental Markov chain Monte Carlo simulation.\u003c\/p\u003e\n\u003cp\u003e* Integrates R code, including RStan modeling tools and the bayesrules package.\u003c\/p\u003e\n\u003cp\u003e* Encourages readers to tap into their intuition and learn by doing.\u003c\/p\u003e\n\u003cp\u003e* Provides a friendly and inclusive introduction to technical Bayesian concepts.\u003c\/p\u003e\n\u003cp\u003e* Supports Bayesian applications with foundational Bayesian theory.\u003c\/p\u003e\n\u003ch3\u003eProductspecificaties\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eAuteur:\u003c\/strong\u003e Alicia A. Johnson\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSerie:\u003c\/strong\u003e Chapman \u0026amp; Hall\/CRC Texts in Statistical Science\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUitgever:\u003c\/strong\u003e Taylor \u0026amp; Francis Ltd\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eImprint:\u003c\/strong\u003e Chapman \u0026amp; Hall\/CRC\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVerschijningsdatum:\u003c\/strong\u003e 2022-03-04\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAantal pagina's:\u003c\/strong\u003e 544\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eISBN:\u003c\/strong\u003e 9780367255398\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eThema:\u003c\/strong\u003e Bayesian inference\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBISAC:\u003c\/strong\u003e MATHEMATICS \/ Probability \u0026amp; Statistics \/ Bayesian Analysis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eOver de auteur\u003c\/h3\u003e\n\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAlicia Johnson\u003c\/b\u003e is an Associate Professor of Statistics at Macalester College in Saint Paul, Minnesota. She enjoys exploring and connecting students to Bayesian analysis, computational statistics, and the power of data in contributing to this shared world of ours.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eMiles Ott\u003c\/b\u003e is a Senior Data Scientist at The Janssen Pharmaceutical Companies of Johnson \u0026amp; Johnson. Prior to his current position, he taught at Carleton College, Augsburg University, and Smith College. He is interested in biostatistics, LGBTQ+ health research, analysis of social network data, and statistics\/data science education. He blogs at milesott.com and tweets about statistics, gardening, and his dogs on Twitter.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eMine Dogucu\u003c\/b\u003e is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. She spends majority of her time thinking about what to teac…\u003c\/p\u003e","brand":"Intertaal","offers":[{"title":"Default Title","offer_id":56354521088340,"sku":"9780367255398","price":81.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0967\/0538\/0692\/files\/9780367255398.jpg?v=1783584641","url":"https:\/\/intertaalid.nl\/de\/products\/bayes-rules-an-introduction-to-applied-bayesian-modeling-alicia-a-johnson-9780367255398","provider":"Intertaal","version":"1.0","type":"link"}