Bayesian Time Series Models

Langue : anglais

Edité par Cambridge University Press, 2011

0521196760 / 9780521196765

  • Éd. originale
  • Livre relié
  • Occasion
Afficher toutes les informations

Vendeur : killarneybooks, Inagh, Clare, Irlandekillarneybooks

Vendeur avec une évaluation de 5 étoiles

Vendeur AbeBooks depuis 20 avril 2017

Afficher les articles de ce vendeur
Livre relié

Etat: Occasion - Assez bon

EUR 56,30

EUR 34,84 expédition 
Expédition depuis Irlande vers Etats-Unis

Quantité disponible : 1 disponible(s)

Ajouter au panier
Retours gratuits sous 30 jours

Item description from seller

Oversized hardcover, xiii + 417pp + 4 pages of plates, shipping weight over 1kg, NOT ex-library. Owner's name inside the front board covered with a blank sticker. Book is clean and bright with unmarked text, free of stamps, firmly bound. Issued without a dust jacket. -- 'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice. -- Contents: 1. Inference and estimation in probabilistic time series models / David Barber, A. Taylan Cemgil & Silvia Chiappa, University of Cambridge; -- I. Monte Carlo -- 2. Adaptive Markov chain Monte Carlo: theory and methods / Yves Atchadé, Gersende Fort, Eric Moulines & Pierre Priouret; 3. Auxiliary particle filtering: recent developments / Nick Whiteley & Adam M. Johansen; 4. Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Omiros Papaspiliopoulos; -- II. Deterministic approximations -- 5. Two problems with variational expectation maximisation for time series models / Richard Eric Turner & Maneesh Sahani; 6. Approximate inference for continuous-time Markov processes / Cédric Archambeau & Manfred Opper; 7. Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Onno Zoeter & Tom Heskes; 8. Approximate inference in switching linear dynamical systems using Gaussian mixtures / David Barber; -- III. Switching models -- 9. Physiological monitoring with factorial switching linear dynamical systems / John A. Quinn & Christopher K.I. Williams; 10. Analysis of changepoint models / Idris A. Eckley, Paul Fearnhead & Rebecca Killick; -- IV. Multi-object models -- 11. Approximate likelihood estimation of static parameters in multi-target models / Sumeetpal S. Singh, Nick Whiteley & Simon J. Godsill; 12. Sequential inference for dynamically evolving groups of objects / Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier & Simon Hill; 13. Non-commutative harmonic analysis in multi-object tracking / Risi Kondor; -- V. Nonparametric models -- 14. Markov chain Monte Carlo algorithms for Gaussian processes / Michalis K. Titsias, Magnus Rattray & Neil D. Lawrence; 15. Nonparametric hidden Markov models / Jurgen Van Gael & Zoubin Ghahramani; 16. Bayesian Gaussian process models for multi-sensor time series prediction / Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn & Nick R. Jennings; -- VI. Agent-based models -- 17. Optimal control theory and the linear Bellman equation / Hilbert J. Kappen; 18. Expectation maximisation methods for solving (PO)MDPs and optimal control problems / Marc Toussaint, Amos Storkey & Stefan Harmeling, Biological Cybernetics; Index.

N° de réf. du vendeur 011058

Titre
Bayesian Time Series Models
Auteur
David Barber; A. Taylan Cemgil; Silvia Chiappa
Éditeur
Cambridge University Press
Année de publication
2011
État de l'article
Very Good
Reliure
Hardcover
Langue
anglais
ISBN à 10 chiffres
0521196760
ISBN à 13 chiffres
9780521196765
Édition
1st Edition

killarneybooks

Inagh, Clare, Irlande

Vendeur avec une évaluation de 5 étoiles

Vendeur AbeBooks depuis 20 avril 2017

Frais d'expédition de Irlande vers Etats-Unis

Article5 à 7 jours ouvrés2 à 4 jours ouvrés
Premier articleEUR 34,84EUR 38,00
Les délais de livraison sont fixés par les vendeurs et varient en fonction du transporteur et du lieu. Les commandes transitant par les douanes peuvent être retardées et les acheteurs sont responsables de tous les droits ou frais associés. Les vendeurs peuvent vous contacter au sujet de frais supplémentaires afin de couvrir toute augmentation des coûts d'expédition de vos articles.

Modes de paiement

  • Visa
  • Mastercard
  • American Express
  • Carte Bleue
  • Apple Pay
  • Google Pay
  • Paypal
  • Virement bancaire

Description de la boutique

All U.S.-bound orders are now shipped with UPS for reliable delivery. Additional customs or import fees are highly unlikely; however, please note buyers remain responsible for any duties or taxes that U.S. Customs may assess. -- Killarneybooks is a family-run bookshop based in the Republic of Ireland. We take pride in accurate listings, careful packaging, and prompt service - most orders ship within 24 hours. All books listed are in stock and ready for immediate dispatch - we are not dropshippers. Inquiries always welcome.

Spécialité

professional, academic, non-fiction