INFPROBMOD · Approximate Inference in Probabilistic Models
FP7 — People (Marie Curie Actions)
- Duration
- 2009-09-01 → 2011-08-31
- EU contribution
- €163,136
- Participants
- 1
- Scheme
- MC-IEF
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Results in brief
Approximate Inference in Probabilistic Models
In this project we have analysed and developed probabilistic models and approximate inference techniques for complex real-world applications. In particular, we have considered two important problems in robotics and bioinformatics: the creation of movement libraries for robot imitation learning, and the incorporation of family structure into genetic data analysis. There were two major achievements of the project: - Introduction of an efficient method, requiring only limited human intervention, for segmenting time-series such as human movement data into basic actions. This represents a relevant contribution towards the goal of creating basic movement libraries, a problem that is receiving increasing attention in the Robotics community. - Extension of a popular genetic data model for genotype imputation and haplotype estimation so as to account for mother-father-child relationships. This extension gives good performance, can be used by experimenters with little knowledge of modelling, and is computational efficient. The underlying approach can also be applied to more complex pedigrees. This work opens the way towards the more general use of deterministic approximate inference techniques for analysing genetic data from related individuals - a type of data that is becoming increasingly common, especially in the studies of complex diseases.
Data: CORDIS, © European Union
Project 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.
Original text from CORDIS.
Participants
- THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE · CAMBRIDGECoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union