
Slice Sampling Particle Belief Propagation
Inference in continuous label Markov random fields is a challenging task...
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Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
Poyiadjis et al. (2011) show how particle methods can be used to estimat...
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Sigma Point Belief Propagation
The sigma point (SP) filter, also known as unscented Kalman filter, is a...
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SelfGuided Belief Propagation  A Homotopy Continuation Method
We propose selfguided belief propagation (SBP) that modifies belief pro...
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Adaptive sequential Monte Carlo by means of mixture of experts
Appropriately designing the proposal kernel of particle filters is an is...
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Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
View synthesis is a process for generating novel views from a scene whic...
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Complexity of full counting statistics of free quantum particles in entangled states
We study the computational complexity of quantummechanical expectation ...
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Expectation Particle Belief Propagation
We propose an original particlebased implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a continuous state space. The algorithm constructs adaptively efficient proposal distributions approximating the local beliefs at each note of the MRF. This is achieved by considering proposal distributions in the exponential family whose parameters are updated iterately in an Expectation Propagation (EP) framework. The proposed particle scheme provides consistent estimation of the LBP marginals as the number of particles increases. We demonstrate that it provides more accurate results than the Particle Belief Propagation (PBP) algorithm of Ihler and McAllester (2009) at a fraction of the computational cost and is additionally more robust empirically. The computational complexity of our algorithm at each iteration is quadratic in the number of particles. We also propose an accelerated implementation with subquadratic computational complexity which still provides consistent estimates of the loopy BP marginal distributions and performs almost as well as the original procedure.
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