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Mathematics, 2022 A difficult and open problem in artificial intelligence is the development of agents that can operate in complex environments which change over time. The present communication introduces the formal notions, the architecture, and the training algorithm of
Edmondo Trentin
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Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds [PDF]
Neural Networks, 1997 We consider the problem of estimating a density function from a sequence identically distributed observations x(i) taking value in X subset R(d). The estimation procedure constructs a convex mixture of "basis" densities and estimates the parameters using the maximum likelihood method.
Assaf J. Zeevi, Ronny Meir
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An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
Ecology and Evolution, 2020 Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture–recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters.
Samantha McFarlane +6 more
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Local density estimation for VANETs [PDF]
2016 Global Information Infrastructure and Networking Symposium (GIIS), 2016 Local vehicle density estimation is an integral part of various applications of Vehicular Ad-hoc Networks (VANETs) such as congestion control and congestion traffic estimation. Currently, many applications use beacons to estimate this density. However, many studies show that the reception rate of these beacons can significantly drop at short distances ...
Noureddine Haouari +3 more
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Tensor-Train Density Estimation
CoRR, 2021 Accepted for the 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021)
Georgii S. Novikov +2 more
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Parameter estimation for stable distributions with application to commodity futures log-returns
Cogent Economics & Finance, 2017 This paper explores the theory behind the rich and robust family of $ \alpha $-stable distributions to estimate parameters from financial asset log-returns data.
M. Kateregga, S. Mataramvura, D. Taylor
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Journal of Statistical Software, 2017 msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric inference introduced by Canale and Dunson (2016). The method, based on mixtures of multiscale beta dictionary densities, overcomes the drawbacks of Pólya trees
Antonio Canale
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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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Wildlife Society Bulletin, 2020 Drones equipped with thermal sensors have shown ability to overcome some of the limitations often associated with traditional human‐occupied aerial surveys (e.g., low detection, high operational cost, human safety risk).
Jared T. Beaver +5 more
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Estimation of Densities and Applications
Journal of Theoretical Probability, 1998 In this paper we show some estimates for the density of a random variable on the Wiener space that satisfies a nondegeneracy condition using the stochastic calculus of variations. The case of a diffusion process is considered, and an application to the solution of a stochastic partial differential equation is discussed.
Caballero, M. E. (María Emilia) +2 more
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