---
title: "Lennart Svensson og Simo Särkkä Bayesian Filtering and Smoothing"
ean: "9781108926645"
brand: "Lennart Svensson og Simo Särkkä"
category: "Medier > Bøger"
price_min: 324
price_max: 324
currency: DKK
vendor_count: 1
in_stock: true
url: https://pricetracker.dk/p/9781108926645/bayesian-filtering-and-smoothing
last_checked: 2026-10-15
---

# Lennart Svensson og Simo Särkkä Bayesian Filtering and Smoothing

EAN 9781108926645 · Mærke: Lennart Svensson og Simo Särkkä · Kategori: Medier > Bøger

Sammenlign priser på Lennart Svensson og Simo Särkkä Bayesian Filtering and Smoothing fra 1 forhandler. Billigste pris nu: 324 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9781108926645/bayesian-filtering-and-smoothing

## Priser
- **Billigste pris nu:** 324 kr hos Saxo DK
- **Prisspænd:** 324 kr – 324 kr på tværs af 1 forhandler

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 324 kr | — |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.

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