Modeling and Mitigating Errors in Belief Propagation for Distributed Detection
نویسندگان
چکیده
We study the behavior of belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts distributed multidimensional hypothesis test over binary random variables. joint statistical observations is modeled Markov field whose parameters are used to build BP messages exchanged between sensing nodes. Through linearization message-update rule, we analyze resulting decision variables and derive closed-form relationships that describe impact stochastic errors on performance algorithm. then develop decentralized optimization framework enhance system mitigating via linear data-fusion scheme. Finally, compare results proposed analysis with existing works visualize, computer simulations, gain obtained optimization.
منابع مشابه
Message Errors in Belief Propagation
Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether from quantization or other simplified message representations or from stochastic approximation methods. Introducing such errors into the BP message computations has the potential to adversely affect the solution ...
متن کاملDistributed Convergence Verification for Gaussian Belief Propagation
Gaussian belief propagation (BP) is a computationally efficient method to approximate the marginal distribution and has been widely used for inference with high dimensional data as well as distributed estimation in large-scale networks. However, the convergence of Gaussian BP is still an open issue. Though sufficient convergence conditions have been studied in the literature, verifying these co...
متن کاملMitigating Tropospheric Propagation Delay Errors in Precise Airborne GPS Navigation
The high spatial and temporal variability of the troposphere is well known, as is its effect − through propagation delays − on GPS positioning. This effect can be particularly problematical in airborne kinematic differential positioning where the altitude difference between reference station and aircraft is typically quite large. The use of zenith delay models and mapping functions at ground st...
متن کاملResidual Belief Propagation for Topic Modeling
Fast convergence speed is a desired property for training latent Dirichlet allocation (LDA), especially in online and parallel topic modeling for massive data sets. This paper presents a novel residual belief propagation (RBP) algorithm to accelerate the convergence speed for training LDA. The proposed RBP uses an informed scheduling scheme for asynchronous message passing, which passes fast-co...
متن کاملOnline Belief Propagation for Topic Modeling
Not only can online topic modeling algorithms extract topics from big data streams with constant memory requirements, but also can detect topic shifts as the data stream flows. Fast convergence speed is a desired property for batch learning topic models such as latent Dirichlet allocation (LDA), which can further facilitate developing fast online topic modeling algorithms for big data streams. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Communications
سال: 2021
ISSN: ['1558-0857', '0090-6778']
DOI: https://doi.org/10.1109/tcomm.2021.3056679