---
title: "Subhashis Ghosal og Aad van der vaart Fundamentals of Nonparametric Bayesian Inference"
ean: "9780521878265"
brand: "Subhashis Ghosal og Aad van der vaart"
category: "Medier > Bøger"
price_min: 734
price_max: 734
currency: DKK
vendor_count: 1
in_stock: true
url: https://pricetracker.dk/p/9780521878265/fundamentals-of-nonparametric-bayesian-inference
last_checked: 2026-10-14
---

# Subhashis Ghosal og Aad van der vaart Fundamentals of Nonparametric Bayesian Inference

EAN 9780521878265 · Mærke: Subhashis Ghosal og Aad van der vaart · Kategori: Medier > Bøger

Sammenlign priser på Subhashis Ghosal og Aad van der vaart Fundamentals of Nonparametric Bayesian Inference fra 1 forhandler. Billigste pris nu: 734 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9780521878265/fundamentals-of-nonparametric-bayesian-inference

## Priser
- **Billigste pris nu:** 734 kr hos Saxo DK
- **Prisspænd:** 734 kr – 734 kr på tværs af 1 forhandler

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 734 kr | — |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.

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