Approximate Inference in Bayesian Networks
The parameterized complexity of approximate inference in Bayesian networks
... Donders Institute for Brain, Cognition and Behaviour Nijmegen, The Netherlands Abstract Computing posterior and marginal probabilities constitutes the backbone of almost all inferences in Bayesian networks. ...
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Approximate inference of Bayesian networks through edge deletion
... new approximate inference algorithms when run on ten different real-world Bayesian ...the approximate posterior probabilities will be from the exact ...
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Learning low inference complexity Bayesian networks
... the inference complexity of the candidate models during the learning process, providing also an exact inference ...exact inference in PTs outperformed those provided by the models learned with a ...
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Learning Bayesian networks with low inference complexity
... The reason for using approximate inference is that the MDL score, that is used in combination with 2iCHC, does not penalize the infer- ence complexity of the models, so the computation[r] ...
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Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines
... Using approximate inference to es- timate the marginal distribution over the last stage in the pipeline, such as our sampling approach, the pipeline length does not have this negative impact or affect the ...
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Structured Bayesian Approximate Inference
... There is also room for exploring some of the more advanced architectures from the ten- sor networks literature. The tensor ring (Zhao et al., 2016), known as an MPS with periodic boundary conditions in the physics ...
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Approximate Decentralized Bayesian Inference
... sensor networks in which the network structure varies over time, agents drop out and are added dynamically, and no single agent has the computational or communication resources to act as a cen- tral hub during ...
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Approximate Bayesian techniques for inference in stochastic dynamical systems
... neural networks (PANN) courses, that I had to undertake as part of my training, was a true “crash test” and proved an invaluable tool for the rest of my ...
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MAP inference in dynamic hybrid Bayesian networks
... exact inference in the unrolled network, and the approximate MAP sequence provided by our scheme can therefore be compared to the correct MAP sequence whenever HUGIN is able to provide a ...the ...
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Structured Dropout Variational Inference for Bayesian neural networks
... 1. maintain the backpropagation in parallel and optimize efficiently with gradient-based methods 2. acquire flexible Bayesian inference in terms of both prior and approximate posterior , but ...
Inference in hybrid Bayesian networks using mixtures of polynomials
... they approximate non-linear deterministic functions by piecewise linear ...describe approximate probability propagation with MTE approximations that have only two exponential terms in each ...
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Variational algorithms for approximate Bayesian inference
... 4.6 Digit experiments In this section we present results of using variational Bayesian MFA to learn both supervised and unsupervised models of images of 8 x 8 digits taken from the CEDAR database (Hull, 1994). ...
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Some Contributions to Approximate Inference In Bayesian Statistics
... fast approximate Bayesian inference for mixture data which is particularly of use when data sets are so large that many standard Markov Chain Monte Carlo (MCMC) algorithms cannot be applied ...
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Approximate Bayesian computation using indirect inference
... In this paper we presented an approach for obtaining summary statistics for use in ABC algo- rithms based on indirect inference. A simpler yet flexible model is proposed that has a tractable likelihood function, ...
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Approximate Bayesian inference for robust speech processing
... 5. Log Spectra Enhancement using Speaker Dependent Priors for Speaker Verification The experimental results presented in the previous chapter showed the perfor- mance gains we can obtain in speech enhancement and speaker ...
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Stochastic Gradient Descent as Approximate Bayesian Inference
... Fig. 1 shows two-dimensional projections of samples from the posterior (blue) and the station- ary distribution (cyan), where the directions were chosen two be the smallest and largest principal component of the ...
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Approximate Bayesian inference methods for stochastic state space models
... to approximate the optimal algorithm for non- linear state-space models with Gaussian noise and we apply such approximations to two examples: a range and bearing tracking problem and an indoor positioning problem ...
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Likelihood-free inference and approximate Bayesian computation for stochastic modelling
... the inference of complex models will be ...several inference alternatives, one is likelihood ...exact inference. For a survey on methods for SDE inference, see ...
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Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
... In the first experiment, we investigate the performance of the proposed Power EP method on toy regression datasets where ground truth is known. We vary α (from 0 VFE to 1 EP/FITC) and the number of pseudo-points (from 5 ...
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Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
... are established there: in a nutshell, while outcomes are qualitatively different, the algorithm behaviour re- mains reasonable. In contrast, if any of the EP algo- rithms discussed in this paper are run with Lanczos ...
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