---
title: "Aki Vehtari, Andrew Gelman, David B. Dunson, Donald B. Rubin, Hal S. Bayesian Data Analysis"
ean: "9781439840955"
brand: "Aki Vehtari, Andrew Gelman, David B. Dunson, Donald B. Rubin, Hal S."
category: "Medier > Bøger > Trykte bøger"
price_min: 979
price_max: 979
currency: DKK
vendor_count: 1
in_stock: true
url: https://pricetracker.dk/p/9781439840955/bayesian-data-analysis
last_checked: 2026-10-15
---

# Aki Vehtari, Andrew Gelman, David B. Dunson, Donald B. Rubin, Hal S. Bayesian Data Analysis

EAN 9781439840955 · Mærke: Aki Vehtari, Andrew Gelman, David B. Dunson, Donald B. Rubin, Hal S. · Kategori: Medier > Bøger > Trykte bøger

Sammenlign priser på Aki Vehtari, Andrew Gelman, David B. Dunson, Donald B. Rubin, Hal S. Bayesian Data Analysis fra 1 forhandler. Billigste pris nu: 979 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9781439840955/bayesian-data-analysis

## Priser
- **Billigste pris nu:** 979 kr hos Saxo DK
- **Prisspænd:** 979 kr – 979 kr på tværs af 1 forhandler

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 979 kr | — |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

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