While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they a...
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Confirmation bias, the tendency to interpret information in a way that aligns with one's preconceptions, can profoundly impact scientific research, leading to conclusions that reflect the researcher's hypothes...
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Combining data from various sources empowers researchers to explore innovative questions, for example those raised by conducting healthcare monitoring studies. However, the lack of a unique identifier often poses chal...
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Independent Component Estimation (ICE) has many applications in modern day machine learning as a feature engineering extraction method. Horseshoe-type priors are used to provide scalable algorithms that enables both p...
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We introduce a novel Bayesian approach for both covariate selection and sparse precision matrix estimation in the context of high-dimensional Gaussian graphical models involving multiple responses. Our approach provid...
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We present the first mini-batch algorithm for maximizing a non-negative monotone decomposable submodular function, (Equation presented), under a set of constraints. We consider two sampling approaches: uniform and wei...
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Bayesian Optimal Experimental Design (BOED) is a powerful tool to reduce the cost of running a sequence of experiments. When based on the Expected Information Gain (EIG), design optimization corresponds to the maximiz...
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Practitioners conducting adaptive experiments often encounter two competing priorities: maximizing total welfare (or 'reward') through effective treatment assignment and swiftly concluding experiments to imple...
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Manual analysis of security events inevitably results in a large number of attacks can not being detected timely This paper presents a new network self-protection system model,gives its architecture and designs its in...
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Manual analysis of security events inevitably results in a large number of attacks can not being detected timely This paper presents a new network self-protection system model,gives its architecture and designs its information security event correlative analysis *** the correlation engine,the expectationmaximization(EM) algorithm was designed to solve the problem by lack of security *** the bucket elimination algorithm was used to enhance the computational efficiency of Bayesian *** experimental results demonstrate that the reasoning speed of the correlation engine fulfills the need of network protection against unknown attacks.
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empiric...
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