19,276 research outputs found
Towards Gaussian Bayesian network fusion
Data sets are growing in complexity thanks to the increasing
facilities we have nowadays to both generate and store data. This poses
many challenges to machine learning that are leading to the proposal of
new methods and paradigms, in order to be able to deal with what is
nowadays referred to as Big Data. In this paper we propose a method
for the aggregation of different Bayesian network structures that have
been learned from separate data sets, as a first step towards mining data
sets that need to be partitioned in an horizontal way, i.e. with respect
to the instances, in order to be processed. Considerations that should be
taken into account when dealing with this situation are discussed. Scalable
learning of Bayesian networks is slowly emerging, and our method
constitutes one of the first insights into Gaussian Bayesian network aggregation
from different sources. Tested on synthetic data it obtains good
results that surpass those from individual learning. Future research will
be focused on expanding the method and testing more diverse data sets
Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks
The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented
for the problem of tracking a target dynamic model using a time-varying network
of heterogeneous sensing agents. In the DBF algorithm, the sensing agents
combine their normalized likelihood functions in a distributed manner using the
logarithmic opinion pool and the dynamic average consensus algorithm. We show
that each agent's estimated likelihood function globally exponentially
converges to an error ball centered on the joint likelihood function of the
centralized multi-sensor Bayesian filtering algorithm. We rigorously
characterize the convergence, stability, and robustness properties of the DBF
algorithm. Moreover, we provide an explicit bound on the time step size of the
DBF algorithm that depends on the time-scale of the target dynamics, the
desired convergence error bound, and the modeling and communication error
bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into
a modified form of the Kalman information filter. The performance and robust
properties of the DBF algorithm are validated using numerical simulations
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