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Showing 1 to 12 of 12 for “"Approximate Bayesian Inference"”.
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Approximate Bayesian inference and optimal transport
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Latent variable augmentation for approximate Bayesian inference
Performing inference on probabilistic models can represent a challenge even in seemingly simple problems. When working with non-conjugate Bayesian models, we need approximate methods such as variational inference or sampling, each with its pitfalls and limits. For instance, heavy-tailed …
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A family of algorithms for approximate Bayesian inference
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
… It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approximate Bayesian learning for a wide variety of …
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Stochastic Modelling and Approximate Bayesian Inference: Applications in Object Tracking and Intent Analysis
As two fundamental pillars of Bayesian inference for time series, stochastic modelling and approximate Bayesian inference play crucial roles in providing accurate priors for underlying random processes and addressing the challenges of evaluating posterior distributions when exact computation is …
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Approximate Inference: New Visions
… confidence. Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable decision …
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Adapting deep neural networks as models of human visual perception
… variational DNNs that use sampling to perform approximate Bayesian inference. In the first investigation, RDL successfully transferred information from a teacher model to a student DNN. This was achieved by driving the student DNN’s representational distance matrix (RDM), which characterises …
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Who Should I Trust? Uncertainty and Risk for Knowledge Transfer from Multiple Sources in Reinforcement Learning Domains
… We address epistemic uncertainty by leveraging Bayesian model combination (BMC) to quantify and utilize uncertainty over the selection of knowledge sources for transfer, and we develop novel analytical techniques to efficiently tackle approximate Bayesian inference to train such models. We …
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Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals
… estimates to be robust is to adopt the Bayesian framework, which offers a principled approach to handling uncertainty. Two of the major contributions in this thesis relate to Bayesian NNs (BNNs). Specifying appropriate priors is an important step in any Bayesian model, yet it is not …
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Numerical approaches for sequential Bayesian optimal experimental design
… data that are optimal for model parameter inference, we develop a rigorous Bayesian formulation for OED using an objective that incorporates a measure of information gain. This framework is first demonstrated in a batch design setting, and then extended to sOED using a dynamic programming …
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Adjoint-accelerated Bayesian inference in thermoacoustics
… modelling, in which we use an efficient Bayesian inference framework to assimilate experimental data into thermoacoustic models. The framework provides four main tools: (i) parameter inference, (ii) uncertainty quantification, (iii) model comparison, and (iv) optimal experiment design. …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… models, and our ability to learn and make inferences from data. Specifically we present theoretical analyses alongside algorithmic and modelling advances in three areas of probabilistic machine learning: sparse Gaussian process approximations and invariant covariance functions, learning …