INFPROBMOD

Approximate Inference in Probabilistic Models

 Coordinatore THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE 

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Edna
Cognome: Murphy
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 0 €
 EC contributo 163˙135 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-IEF-2008
 Funding Scheme MC-IEF
 Anno di inizio 2009
 Periodo (anno-mese-giorno) 2009-09-01   -   2011-08-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Edna
Cognome: Murphy
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

UK (CAMBRIDGE) coordinator 163˙135.67

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

sequential    solve    bayesian    probabilistic    techniques    lgssms    technique    analyze    inference    approximation    models    approximate   

 Obiettivo del progetto (Objective)

'We propose to develop and analyze approximate inference methods for probabilistic models. Probabilistic models are widely used in Machine Learning to solve complex real-world problems and they also form an important research area in Statistics. One of the biggest challenges in probabilistic modelling is to be able to infer marginal probabilities of some random variables in the model, a task which is often formally computationally intractable due to the complexity of the situation modeled. We propose to contribute to the advancement in developing and understanding the properties of approximate inference techniques through three important research objectives. The first objective is to develop and analyze approximate inference techniques for Bayesian Linear Gaussian State-Space based Models (LGSSMs). LGSSMs are used in many application domains and we recently developed a Bayesian approach to a class of models based on LGSSMs using a deterministic approximation technique. We would like to investigate more in dept the properties of the proposed technique and develop other approximation techniques which have different characteristics. The second objective is to perform a theoretical evaluation and a more exhaustive experimental comparison of the the state-of-the-art algorithm for approximate inference in LGSSMs with switching dynamics, and investigate the extension of this approximation technique to other related models. The third objective is to develop inference methods in sequential decision theory, by exploiting the new point-of-view which sees planning problems as inference problems in probabilistic models. We would like to concentrate on Markovian models not yet analyzed, and to apply the resulting methods to solve imitation problems in robotics and to design optimal sequential experiments in bioinformatics and chemoinformatics. This project has the potential to contribute towards technological advances in a large spectrum of applications.'

Altri progetti dello stesso programma (FP7-PEOPLE)

REN2010 (2010)

Researchers' Night 2010: The Greek Events

Read More  

STRAINMAP (2008)

3D contour and strain mapping for non-destructive evaluation of engineering components

Read More  

DENSE4GREEN (2013)

Dense Deployments for Green Networks

Read More