Smart grid envisions the potential to manage diverse energy resources and enable a future self-dispatch and selfhealing grid. This would first require the micro-grid visibility of node behavior (i.e. electrical parameters). In this paper we propose a novel approach to construct a stochastic model that makes global inference on every node at the micro-grid level. The micro-grid system can be mod...

If several friends of Smith have committed petty thefts, what would you say about Smith? Most people would not be surprised if Smith is a hardened criminal. Guilt-by-association methods combine weak signals to derive stronger ones, and have been extensively used for anomaly detection and classification in numerous settings (e.g., accounting fraud, cyber-security, calling-card fraud). The focus ...

Belief Propagation is a well-studied message-passing algorithm that runs over graphical models and can be used for approximate inference approximation of local marginals. The resulting approximations are equivalent to the Bethe-Peierls statistical mechanics. Here we show how this adapted world PEPS tensor networks as an contraction scheme. We further resultant ``mean field'' in Simple-Update al...

Belief propagation and its variants are popular methods for approximate inference, but their running time and even their convergence depend greatly on the schedule used to send the messages. Recently, dynamic update schedules have been shown to converge much faster on hard networks than static schedules, namely the residual BP schedule of Elidan et al. [2006]. But that RBP algorithm wastes mess...

A BP-based algorithm with 2-dimensional classified normalized correction is developed to reduce the complexity and improve the performance of decoding algorithm for the low density parity check (LDPC) codes. The algorithm first utilizes classification according to the absolute values of incoming messages in check nodes. Then it uses 2-dimensional normalization to correct the minimum and sub-min...

Reputation systems seek to infer which members of a community can be trusted based on ratings they issue about each other. We construct a Bayesian inference model and simulate approximate estimates using belief propagation (BP). The model is then mapped onto computing equilibrium properties of a spin glass in a random field and analyzed by employing the replica symmetric cavity approach. Having...

We proposed an iterative message passing receiver, which jointly performs channel state information and noise precision estimation and decoding, for OFDM systems. A message passing framework, which combines belief propagation (BP) and mean field (MF), is exploited on a stretched factor graph, where MF message update rule is used for observation factor nodes, so that noise precision (the recipro...

Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixed-points of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP is guaranteed to converge only for acyclic graph models. Moreover, the Bethe free energy is nonconvex for gra...

In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-todate medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, covering a large part of internal medicine. We show that Belief Propagation (BP) can be successfully applied as approximate inferenc...

In this paper, we present a belief propagation based global algorithm that generates high quality results while maintaining real-time performance. To our knowledge, it is the first BP based global method that runs at real-time speed. Our efficiency performance gains mainly from the parallelism of graphics hardware,which leads to a 45 times speedup compared to the CPU implementation. To qualify ...