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Condición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: New.

Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Condición: New. pp. 521.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Condición: New. pp. 521.

Paperback. Condición: New. 1st. An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling 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 theyare 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.Features. Utilizes data-driven examples and exercises. Emphasizes the iterative model building and evaluation process. Surveys an interconnected range of multivariable regression and classification models. Presents fundamental Markov chain Monte Carlo simulation. Integrates R code, including RStan modeling tools and the bayesrules package. Encourages readers to tap into their intuition and learn by doing. Provides a friendly and inclusive introduction to technical Bayesian concepts. Supports Bayesian applications with foundational Bayesian theory.…

Condición: New. pp. 521.

Condición: New. Alicia Johnson 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 share.

Paperback. Condición: Brand New. 552 pages. 10.00x7.00x1.14 inches. In Stock.

Paperback. Condición: New. 1st. An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling 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 theyare 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.Features. Utilizes data-driven examples and exercises. Emphasizes the iterative model building and evaluation process. Surveys an interconnected range of multivariable regression and classification models. Presents fundamental Markov chain Monte Carlo simulation. Integrates R code, including RStan modeling tools and the bayesrules package. Encourages readers to tap into their intuition and learn by doing. Provides a friendly and inclusive introduction to technical Bayesian concepts. Supports Bayesian applications with foundational Bayesian theory.…

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: New.

Condición: New. In English.

Hardcover. Condición: Brand New. 552 pages. 10.00x7.00x1.22 inches. In Stock.

Paperback. Condición: Brand New. 552 pages. 10.00x7.00x1.14 inches. In Stock. This item is printed on demand.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Alicia Johnson 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 share. …