Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 24 for “"exact inference"”.
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Efficient Lagrangian relaxation algorithms for exact inference in natural language tasks
… Solving these combinatorial search problems exactly can be inefficient, and so researchers often use approximate techniques at the cost of model accuracy. In this thesis, we turn to Lagrangian relaxation as an alternative to approximate inference in natural language tasks. We demonstrate that …
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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data
We develop new methods of Bayesian inference, largely in the context of analysis of neuroscience data. The work is broken into several parts. In the first part, we introduce a novel class of joint probability distributions in which exact inference is tractable. Previously it has been difficult to …
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Approximating the Log-Partition Function
… from a large number of NP-hard problems to inference tasks such as computing the partition function (exact inference) or approximating the log-partition function (approximate inference). In this master thesis, we will motivate the need for a general constant-factor approximations of the …
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Constrained Information Exchange Message Passing Algorithm in Probabilistic Graphical Models
… tractable, computationally efficient, and exact inference algorithms. Posterior inference using message-passing algorithm depends heavily on the information exchange between the nodes of the graph and having unconstrained sized messages, results in the exact inference of the posterior …
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Feedback message passing for inference in Gaussian graphical models
… nodes use a different message update scheme. Exact inference results can be obtained in O(k²n), where k is the number of feedback nodes and n is the total number of nodes. For graphs with large feedback vertex sets, we describe a tractable approximate feedback message passing algorithm. …
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Inference Plans for Hybrid Probabilistic Inference
… programming languages (PPLs) use hybrid inference systems to combine symbolic exact inference and Monte Carlo sampling to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are represented symbolically and …
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Inference in Ising models by graph neural networks with structural features
… between random variables. The two major inference tasks on PGMs are marginal probability inference and maximum-a-posteriori (MAP) inference. Exact inference on PGMs is intractable, hence approximation algorithms, such as belief propagation, are proposed for practical applications. …
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Large Scale Exact Gaussian Processes Inference and Euclidean Constrained Neural Networks with Physics Priors
… First, we present a practical method to scale exact inference with Gaussian processes to over a million data points using GPU parallelism, a hundred times more than previous methods. In addition, our method outperforms other scalable Gaussian processes while maintaining similar or faster …
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Approximate inference in graphical models
… of complex hierarchical models for real-world inference tasks. Unfortunately, exact inference in probabilistic models is often computationally expensive or even intractable. A close inspection in such situations often reveals that computational bottlenecks are confined to certain aspects of the …
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Efficient Multi-Target Tracking using graphical models
… as false alarms and missed detections. Although exact inference is intractable in graphs with a mix of both discrete and continuous random variables, such as the ones for MTT, our message passing algorithms utilize e±cient particle reduction techniques to make approximate inference tractable on …
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Practical probabilistic inference
… manifestation of disease in a patient. Exact inference of posterior distributions over the disease nodes is extremely intractable using generic algorithms. Inference can be made much more efficient by exploiting the QMR-DT's unique structure. Indeed, tailor-made inference algorithms for …
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Efficient Machine Learning with High Order and Combinatorial Structures
… to develop the representational frameworks, the inference algorithms, and the learning methods necessary for the accurate modeling of domains that exhibit complex and non-local dependency structures. There are three parts to this thesis. In the first part, we develop a toolbox of high order …
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Toward more scalable structured models
… model complexity usually leads to increased inference complexity and trickier learning procedures. Also, making progress on real-world applications requires learning paradigms that circumvent the limitation of evaluating the partition function and scale to high-dimensional datasets. In this …
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A Systems Framework for Multi-Messenger Astronomy
… and cosmic rays — to form astrophysical inferences. Since each messenger interacts uniquely with the dynamics of the phenomena in question, drawing information from multiple messengers poses a more complete probe of the universe. However, the exact inference method is scenario-specific, …
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Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
… obtaining principled solutions to probabilistic inference and analysis queries? This thesis presents efficient techniques for addressing these fundamental challenges grounded in probabilistic programming, that is, by representing probabilistic models as computer programs in specialized …
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Reasoning with models of probabilistic knowledge over probabilistic knowledge
… probabilistic beliefs of agents. We provide exact inference algorithms for the two representations, together with correctness results, and show that they are faster than comparable previous ones when some structural conditions hold. We also present sampling-based algorithms, show that those …
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Exact Sampling and Optimisation in Statistical Machine Translation
In Statistical Machine Translation (SMT), inference needs to be performed over a high-complexity discrete distribution de ned by the intersection between a translation hypergraph and a target language model. This distribution is too complex to be represented exactly and one typically resorts to …
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Uncertainty Quantification and Structure Discovery for Scalable Behavior Science
… observation noise. In developing sampling-based inference for NPP, we discover a novel and efficient Gibbs decomposition for prior distributions on SE(D), the manifold of rigid transformations. We show that NPP learns intuitive part segmentations for diverse objects and enables both analysis and …
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A sampling technique based on LDPC codes
Given an inference problem, it is common that exact inference algorithms are computationally intractable and one has to resort to approximate inference algorithms. Monte Carlo methods, which rely on repeated sampling of the target distribution to obtain numerical results, is a powerful and popular …
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Advances in Software and Spatio-Temporal Modelling with Gaussian Processes
… turn leads to a design for software, including exact and approximate inference algorithms. I demonstrate the utility of this software through a collection of worked examples, focussing on models which are more cleanly and easily expressed using this new software. Secondly, I develop a new …
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