The Expectation Propagation algorithm
Low Complexity Multi-User MIMO Detection for Uplink SCMA System Using Expectation Propagation Algorithm
... passing algorithm (MPA) is used for data detector at the receiver ...detector algorithm named the expectation propagation algorithm (EPA) for ...
7
Training Factored PCFGs with Expectation Propagation
... We use a structured expectation propagation algorithm that makes use of the factored struc- ture in two ways. First, by partitioning the fac- tors, it speeds up parsing exponentially over the ...
11
Expectation Propagation for Rectified Linear Poisson Regression
... robust Expectation Propagation algorithm entirely based on analytically tractable computations operating re- liably in regimes where quadrature based implementations can ...Keywords: ...
17
A Novel Measurement Processing Approach to the Parallel Expectation Propagation Unscented Kalman Filter
... the expectation propagation ...processing expectation propagation unscented Kalman filter is ...novel algorithm is in the ability to achieve computational improvements with negligible ...
7
The EM algorithm, variational approximations and expectation propagation for mixtures
... inevitable, and much space has been devoted to the idea of approximating the posterior density or predictive density of interest stochastically, in the form of a set of realisations from the distribution, typically ...
30
Expectation propagation
... Other constraints are possible: any choice of Ω i for which the computation of (3) is tractable leads to an EP algorithm.... This is why EP is sometimes called ‘moment matching.’..[r] ...
Stochastic Expectation Propagation
... stochastic expectation propagation method for reducing EP’s large memory consumption which is prohibitive for large ...new algorithm to a number of existing methods including assumed density ...
18
Expectation propagation as a solution for digital communication systems.
... 6. CONCLUSION AND FUTURE WORK The design of efficient equalizers is a challenging open prob- lem. In this paper, we focus not only on symbol estimation but also the posterior probability estimation for each received ...
161
Nonconvex image reconstruction via expectation propagation
... V. DISCUSSION We have shown how to address the problem of recon- structing tomographic images by including nonstandard prior information about the image, normally resulting in non-log- concave prior weight functions. The ...
13
Penalized Expectation Propagation for Graphical Models over Strings
... penalized expectation propaga- tion (PEP), a novel algorithm for approximate inference in graphical ...models. Expectation propagation is a variant of loopy belief prop- agation that keeps ...
11
Expectation Propagation for Neural Networks with Sparsity-Promoting Priors
... EP algorithm that utilizes independent approximations for the weights associated with the different hidden units and layers to achieve computational complexity scaling similar to an ensemble of K sparse linear ...
53
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
... the algorithm we propose ...parallel algorithm, we cannot establish this ...convergent algorithm by com- bining optimistic steps min θ ˜ φ(z, ˜ θ) for fixed z with the rigorous but slow mechanism of ...
9
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version
... Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the phenomena underlying ...
12
Iterative Equalization Based on Expectation Propagation: a Frequency Domain Approach
... The factor graph and the exchanged messages alone cannot yield a receiver algorithm without a scheduling scheme for the update of variable and factor nodes. Here, a robust and flexible double-loop FDE structure is ...
6
Deep Gaussian processes for regression using approximate expectation propagation
... Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian pro- cesses (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are ...
28
Gaussian process regression with censored data using expectation propagation
... 4 Related work In recent years researchers have striven to de- vise regression techniques that do not rely on the classical assumptions of parametric meth- ods. In this paper we extended the Gaussian process framework, a ...
9
Deep Gaussian processes using expectation propagation and Monte Carlo methods
... sampling algorithm [Vafa, 2016] to evaluate the log ...sampling algorithm does not set any distribution over the inducing outputs (assumes the inducing outputs are fixed), and therefore they are included as ...
72
Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
... EP algorithm with several ...EP algorithm to various quadrature-based EP methods with respect to the approximate marginal distributions of the latent values and class probabilities with fixed hyperparameter ...
35
Dealing with Massive Data with a Distributed Expectation Propagation Particle Filter for Object Tracking
... the expectation propagation framework, that is capable of dealing with the challenges presented by a large volume of measurements in a distributed ...proposed algorithm, the measurements are ...
8
Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation
... Gaussian expectation propagation (EP) [8], [9] and BP is ...unstable algorithm due to the fact that computed Gaussian EP messages may have a negative ...the algorithm need to be tuned in ...
6