In this article we consider robust filtering and smoothing for Markov Modulated Poisson Processes (MMPPs). Using the EM algorithm, these filters and smoothers can be applied to estimate the parameters of our model. Ou...
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ISBN:
(纸本)0780366387
In this article we consider robust filtering and smoothing for Markov Modulated Poisson Processes (MMPPs). Using the EM algorithm, these filters and smoothers can be applied to estimate the parameters of our model. Our dynamics do not involve stochastic integrals and our new formulae, in terms of time integrals, are easily discretized.
Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considering the dependencies or, preferably, ca...
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Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considering the dependencies or, preferably, causal relations among variables. Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirically shown to be sub-optimal. To address this problem, we propose a score-based algorithm for learning causal structures from missing data based on optimal transport. This optimal transport viewpoint diverges from existing score-based approaches that are dominantly based on expectationmaximization. We formulate structure learning as a density fitting problem, where the goal is to find the causal model that induces a distribution of minimum Wasserstein distance with the observed data distribution. Our framework is shown to recover the true causal graphs more effectively than competing methods in most simulations and real-data settings. Empirical evidence also shows the superior scalability of our approach, along with the flexibility to incorporate any off-the-shelf causal discovery methods for complete data. Copyright 2024 by the author(s)
Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of the improved estimation accuracy and reduced computational cost. However, few consider the existence of neste...
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We propose a new model for metaphor detection in which an expectation component estimates representations of expected word meanings in a given context, whereas a realization component computes representations of targe...
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Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative Expectat...
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This work addresses submodular maximization problems, a widely-used mathematical tool to model many real-world decisions. Though this set of problems is NP-Hard, a well-known result is that a distributed greedy algori...
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This paper addresses the challenge of detecting multiple targets embedded in Gaussian noise with unknown range positions and angles of arrival. To this end, we introduce the signal classification model of interest res...
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Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of their vertices. Several structural learning routines have been defined and implemented ...
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Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and pote...
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Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and potentially mismatched. Current approaches in this sphere often presuppose rigid assumptions regarding the class distribution of unlabeled data, thereby limiting the adaptability of models to only certain distribution ranges. In this study, we propose a novel approach, introducing a highly adaptable framework, designated as SimPro, which does not rely on any predefined assumptions about the distribution of unlabeled data. Our framework, grounded in a probabilistic model, innovatively refines the expectation-maximization (EM) algorithm by explicitly decoupling the modeling of conditional and marginal class distributions. This separation facilitates a closed-form solution for class distribution estimation during the maximization phase, leading to the formulation of a Bayes classifier. The Bayes classifier, in turn, enhances the quality of pseudo-labels in the expectation phase. Remarkably, the SimPro framework not only comes with theoretical guarantees but also is straightforward to implement. Moreover, we introduce two novel class distributions broadening the scope of the evaluation. Our method showcases consistent state-of-the-art performance across diverse benchmarks and data distribution scenarios. Our code is available at https://***/LeapLabTHU/SimPro. Copyright 2024 by the author(s)
In lunar exploration and research, the accurate identification and localization of lunar surface features is of great scientific significance and application value. However, target detection in lunar surface images fa...
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