Probabilistic Answer Set Programming under the credal semantics extends Answer Set Programming with probabilistic facts that represent uncertain information. The probabilistic facts are discrete with Bernoulli distrib...
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Probabilistic Answer Set Programming under the credal semantics extends Answer Set Programming with probabilistic facts that represent uncertain information. The probabilistic facts are discrete with Bernoulli distributions. However, several real-world scenarios require a combination of both discrete and continuous random variables. In this paper, we extend the PASP framework to support continuous random variables and propose Hybrid Probabilistic Answer Set Programming. Moreover, we discuss, implement, and assess the performance of two exact algorithms based on projected answer set enumeration and knowledge compilation and two approximate algorithms based on sampling. Empirical results, also in line with known theoretical results, show that exactinference is feasible only for small instances, but knowledge compilation has a huge positive impact on performance. Sampling allows handling larger instances but sometimes requires an increasing amount of memory.
Prevalence of a disease is usually assessed by diagnostic tests that may produce false results. Rogan and Gladen (1978) described a method to estimate the true prevalence correcting for sensitivity and specificity of ...
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Prevalence of a disease is usually assessed by diagnostic tests that may produce false results. Rogan and Gladen (1978) described a method to estimate the true prevalence correcting for sensitivity and specificity of the diagnostic procedure, and Reiczigel et al. (2010) provided exact confidence intervals for the true prevalence assuming sensitivity and specificity were known. In this paper we propose a new method to construct approximate confidence intervals for the true prevalence when sensitivity and specificity are estimated from independent samples. To improve coverage we applied an adjustment similar to that described in Agresti and Coull (1998). According to an extensive simulation study the new confidence intervals maintain the nominal level fairly well even for sample sizes as small as 30;minimum coverage is above 88%, 93%, and 98% at nominal 90%, 95%, and 99%, respectively. We illustrate the advantages of the proposed method with real-life applications. (C) 2013 Elsevier B.V. All rights reserved.
Images usually contain multiple objects that are semantically related to one another. Mapping from low-level visual features to mutually dependent high-level semantics can be formulated as a structured prediction prob...
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Images usually contain multiple objects that are semantically related to one another. Mapping from low-level visual features to mutually dependent high-level semantics can be formulated as a structured prediction problem. Current statistical models for structured prediction make simplifying assumptions about the underlying output graph structure, such as assuming a low-order Markov chain, because exactinference becomes intractable as the tree-width of the underlying graph increases. approximateinference algorithms, on the other hand, force one to trade off representational power with computational efficiency. In this paper, we present large margin sigmoid belief networks (LMSBNs) for structured prediction in images. LMSBNs allow a very fast inference algorithm for arbitrary graph structures that runs in polynomial time with high probability. This probability is data-distribution dependent and is maximized in learning. The new approach overcomes the representation-efficiency trade-off in previous models and allows fast structured prediction with complicated graph structures. We present results from applying a fully connected model to semantic image annotation, image retrieval and optical character recognition (OCR) problems, and demonstrate that the proposed approach can yield significant performance gains over current state-of-the-art methods.
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