---
title: "Doing Bayesian Data Analysis - A Tutorial with R, JAGS, and Stan - John Kruschke - Hardback Bog - Elsevier Science Publishing Co Inc - 2014 - Engelsk - Booktok.dk"
ean: "9780124058880"
brand: "Elsevier Science Publishing Co Inc"
category: "Medier > Bøger"
price_min: 599
price_max: 698
currency: DKK
vendor_count: 2
in_stock: true
url: https://pricetracker.dk/p/9780124058880/doing-bayesian-data-analysis-a-tutorial-with-r-jags-and-stan-john
last_checked: 2026-10-15
---

# Doing Bayesian Data Analysis - A Tutorial with R, JAGS, and Stan - John Kruschke - Hardback Bog - Elsevier Science Publishing Co Inc - 2014 - Engelsk - Booktok.dk

EAN 9780124058880 · Mærke: Elsevier Science Publishing Co Inc · Kategori: Medier > Bøger

Sammenlign priser på Doing Bayesian Data Analysis - A Tutorial with R, JAGS, and Stan - John Kruschke - Hardback Bog - Elsevier Science Publishing Co Inc - 2014 - Engelsk - Booktok.dk fra 2 forhandlere. Billigste pris nu: 599 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9780124058880/doing-bayesian-data-analysis-a-tutorial-with-r-jags-and-stan-john

## Priser
- **Billigste pris nu:** 599 kr hos Saxo DK
- **Prisspænd:** 599 kr – 698 kr på tværs af 2 forhandlere

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 599 kr | — |
| Booktok | 698 kr | 1-3 dage |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

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