|Title:||Lifted Variational Inference
|Subject(s):||Lifted Variational Inference, Relational Model, Markov Logic Network, First Order Probabilistic Inference
|Abstract:||We present a lifted inference algorithm for relational hybrid
graphical models. Hybrid graphical models with continuous and discrete
variables naturally represent many real-world applications in
robotics, financial market predictions, and weather
analysis. Inference with such large models is challenging because
relational structures deteriorate rapidly with current inference
procedures. The main contribution of this paper is a relational
variational-inference lemma that enables factoring density functions
into a mixture of independent identically distributed multi-valued
Bernoulli trials. This lemma enables a relational factoring step that
takes hybrid ground potentials and finds a close to optimal lifted
relational model for the joint density. This step is then used for
efficient inference without referring to ground random variables. The
new method allows us to build various efficient inference algorithms.
As an example, we provide a lifted Markov Chain Monte Carlo (MCMC)
algorithm that requires fewer samples and generates each sample faster
than possible before. We provide an error analysis of the variational
method when applying to relational models. Our approach is applicable
to general large relational models.
|Peer Reviewed:||not peer reviewed
|Sponsor:||This work was supported in part by NSF award IIS-09-17123 -RI: Scaling Up Inference in Dynamic Systems with Logical Structure and NSF award ECS-09-43627 - Improving Prediction of Subsurface Flow and Transport through Exploratory Data Analysis and Complementary Modeling.
|Date Available in IDEALS:||2012-05-06