{"product_id":"statistical-rethinking-a-bayesian-course-with-examples-in-r-and-stan-chapman-and-hall-crc-texts-in-statistical-science","title":"Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman and Hall\/CRC Texts in Statistical Science)","description":"\u003cp\u003e\u003cstrong\u003eStatistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman and Hall\/CRC Texts in Statistical Science)\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan class=\"a-text-bold\"\u003eWinner of the 2023 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eStatistical Rethinking: A Bayesian Course with Examples in R and Stan\u003c\/span\u003e\u003cspan\u003e builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan class=\"a-text-bold\"\u003eFeatures\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eIntegrates working code into the main text.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eIllustrates concepts through worked data analysis examples.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eEmphasizes understanding assumptions and how assumptions are reflected in code.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eOffers more detailed explanations of the mathematics in optional sections.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePresents examples of using the dagitty R package to analyze causal graphs.\u003cbr\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eProvides the rethinking R package on the author's website and on GitHub.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Chapman and Hall\/CRC","offers":[{"title":"Default Title","offer_id":65077089599865,"sku":"SNG9161","price":59.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0274\/6482\/6995\/files\/81cEpsiTCFL.jpg?v=1793615334","url":"https:\/\/www.books4people.co.uk\/products\/statistical-rethinking-a-bayesian-course-with-examples-in-r-and-stan-chapman-and-hall-crc-texts-in-statistical-science","provider":"books4people","version":"1.0","type":"link"}