Results 41 to 50 of about 282,540 (265)
Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
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A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems
Future Internet, 2022 Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they
Sonya Leech +2 more
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DWT based non-parametric texture modeling [PDF]
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002 We propose a non-parametric texture modeling and synthesis technique based on the integer version of the discrete wavelet transform (DWT). The successive levels of the DWT pyramid of the input texture are progressively sampled starting from the signal approximation to generate the analogous wavelet pyramid for the synthetic texture.
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Advances in Electrical and Electronic Engineering, 2021 Due to the rising usage of various broadcasting systems and web-casting applications, a measurement of audio quality has become an essential task. This paper presents a benchmark of the parametric models for non-intrusive estimation of the audio quality ...
Martin Jakubik, Peter Pocta
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A Non-Parametric Generative Model for Human Trajectories [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Modeling human mobility and synthesizing realistic trajectories play a fundamental role in urban planning and privacy-preserving location data analysis. Due to its high dimensionality and also the diversity of its applications, existing trajectory generative models do not preserve the geometric (and more importantly) semantic features of human ...
Kun Ouyang +3 more
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Non-parametric Models with Instrumental Variables [PDF]
, 2011 This chapter gives a survey of econometric models characterized by a relation between observable and unobservable random elements where these unobservable terms are assumed to be independent of another set of observable variables called instrumental variables.
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A Bayesian natural cubic B-spline varying coefficient method for non-ignorable dropout
BMC Medical Research Methodology, 2020 Background Dropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account
Camille M. Moore +3 more
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On the pitfalls of Gaussian likelihood scoring for causal discovery
Journal of Causal Inference, 2023 We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
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Non-parametric and light-field deformable models [PDF]
Computer Vision and Image Understanding, 2006 Statistical shape-and-texture appearance models use image morphing to define a rich, compact representation of object appearance. They are useful in a variety of applications including object recognition, tracking and segmentation. These techniques, however, have been limited to objects with Lambertian surface reflectance, simple geometry and topology.
C. Mario Christoudias +2 more
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Bayesian non-parametrics and the probabilistic approach to modelling [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013 Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model.
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