Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing
Author :
Publisher : Cambridge University Press
Total Pages : 437
Release :
ISBN-10 : 9781108926645
ISBN-13 : 1108926649
Rating : 4/5 (649 Downloads)

Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2023-06-15 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.


Bayesian Filtering and Smoothing Related Books

Bayesian Filtering and Smoothing
Language: en
Pages: 437
Authors: Simo Särkkä
Categories: Mathematics
Type: BOOK - Published: 2023-06-15 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

A Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Bayesian Filtering and Smoothing
Language: en
Pages: 255
Authors: Simo Särkkä
Categories: Computers
Type: BOOK - Published: 2013-09-05 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Particle Filters for Random Set Models
Language: en
Pages: 184
Authors: Branko Ristic
Categories: Technology & Engineering
Type: BOOK - Published: 2013-04-15 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochas
Multivariate Bayesian Statistics
Language: en
Pages: 350
Authors: Daniel B. Rowe
Categories: Mathematics
Type: BOOK - Published: 2002-11-25 - Publisher: CRC Press

DOWNLOAD EBOOK

Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the