Results 31 to 40 of about 284,592 (266)
Energies, 2020 Since the electricity market liberalisation of the mid-1990s, forecasting energy demand and prices in competitive markets has become of primary importance for energy suppliers, market regulators and policy makers.
Mauro Bernardi, Francesco Lisi
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USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets.
M. Ahmadlou +3 more
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Non-Parametric Learning of Gaifman Models
CoRR, 2020 We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relational features are first-order rules that are then partially grounded and counted over local neighborhoods of a Gaifman model to obtain the feature representations.
Devendra Singh Dhami +3 more
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Functional non-parametric mixed effects models for cytotoxicity assessment and clustering
Scientific Reports, 2023 A multitude of natural and synthetic chemicals are present in our environment.Through the study of a compound’s cytotoxicity, researchers can carefully set regulations regarding how much of a certain chemical in the ambient environment is tolerable.
Tiantian Ma +4 more
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Bayesian Non-Parametric Mixtures of GARCH(1,1) Models
Journal of Probability and Statistics, 2012 Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes ...
John W. Lau, Ed Cripps
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The influence of vegetation height heterogeneity on forest and woodland bird species richness across the United States. [PDF]
PLoS ONE, 2014 Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity.
Qiongyu Huang +3 more
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Non-parametric Models for Non-negative Functions
CoRR, 2020 Linear models have shown great effectiveness and flexibility in many fields such as machine learning, signal processing and statistics. They can represent rich spaces of functions while preserving the convexity of the optimization problems where they are used, and are simple to evaluate, differentiate and integrate.
Marteau-Ferey, Ulysse +2 more
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Non-parametric Bayesian Constrained Local Models [PDF]
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014 This work presents a novel non-parametric Bayesian formulation for aligning faces in unseen images. Popular approaches, such as the Constrained Local Models (CLM) or the Active Shape Models (ASM), perform facial alignment through a local search, combining an ensemble of detectors with a global optimization strategy that constraints the facial feature ...
Pedro Martins 0004 +2 more
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An algorithm for non-parametric estimation in state–space models [PDF]
Computational Statistics & Data Analysis, 2021 State-space models are ubiquitous in the statistical literature since they provide a flexible and interpretable framework for analyzing many time series. In most practical applications, the state-space model is specified through a parametric model. However, the specification of such a parametric model may require an important modeling effort or may ...
Chau, Thi Tuyet Trang +2 more
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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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