Search results for "Approx"
showing 10 items of 922 documents
Modelling Inoculum Availability ofPlurivorosphaerella nawaein Persimmon Leaf Litter with Bayesian Beta Regression
2019
AbstractCircular leaf spot (CLS), caused byPlurivorosphaerella nawae, is a serious disease of persimmon (Diospyros kaki) inducing necrotic lesions on leaves, defoliation and fruit drop. Under Mediter-ranean conditions,P. nawaeforms pseudothecia in the leaf litter during winter and ascospores are released in spring infecting susceptible leaves. Persimmon growers are advised to apply fungicides for CLS control during the period of inoculum availability, which was defined based on ascospore counts under the microscope. A model of inoculum availability ofP. nawaewas developed and evaluated as an alternative to ascospore counts. Leaf litter samples were collected weekly in L’Alcúdia from 2010 to…
Approximation properties of higher degree F-transforms based on B-splines
2015
The paper deals with the F-transform with polynomial components with respect to a generalized fuzzy partition given by B-splines. We investigate approximation properties of the inverse F-transform in this case and prove that using B-splines allows us to improve the quality of approximation of smooth functions.
Evanescent wave approximation for non-Hermitian Hamiltonians
2020
The counterpart of the rotating wave approximation for non-Hermitian Hamiltonians is considered, which allows for the derivation of a suitable effective Hamiltonian for systems with some states undergoing decay. In the limit of very high decay rates, on the basis of this effective description we can predict the occurrence of a quantum Zeno dynamics, which is interpreted as the removal of some coupling terms and the vanishing of an operatorial pseudo-Lamb shift.
Forward and backward diffusion approximations for haploid exchangeable population models
2001
Abstract The class of haploid population models with non-overlapping generations and fixed population size N is considered such that the family sizes ν1,…,νN within a generation are exchangeable random variables. A criterion for weak convergence in the Skorohod sense is established for a properly time- and space-scaled process counting the number of descendants forward in time. The generator A of the limit process X is constructed using the joint moments of the offspring variables ν1,…,νN. In particular, the Wright–Fisher diffusion with generator Af(x)= 1 2 x(1−x)f″(x) appears in the limit as the population size N tends to infinity if and only if the condition lim N→∞ E((ν 1 −1) 3 )/(N Var …
Thermal analysis and new insights to support decision making in retrofit and relaxation of heat exchanger networks
2011
International audience; Pinch analysis offers a rational framework for identifying energy saving targets and designing efficient heat recovery networks, especially in process industry. Several scientists have contributed to improve and automate the original pinch method over the last decades, increasing its capability to deal with a number of specific issues; the expertise of the analyst, however, remains determinant in achieving optimal results. In this paper a procedure for retrofit of existing networks is proposed, based on an integrate use of several techniques (either existing or innovative). The diagnosis of the existing network and of a "Minimum Energy Requirement" configuration emer…
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
2020
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We consider an approach using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure its sufficient mixing, and post-processing the output leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators, and propose an adaptive approximate Bayesi…
Denoising Autoencoders for Fast Combinatorial Black Box Optimization
2015
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered pro…
Efficient Nonlinear RX Anomaly Detectors
2020
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks t…
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
2019
The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called reference points. Its simple formulation has attracted several recent works on extensions and applications. In this paper, we aim to address some open questions related to the MLM. First, we detail theoretical aspects that assure the interpolation and universal approximation capabilities of the MLM, which were previously only empirically verified. Second, we identify the task of selecting reference points as having major importance for the MLM's generaliz…
Compressed Particle Methods for Expensive Models With Application in Astronomy and Remote Sensing
2021
In many inference problems, the evaluation of complex and costly models is often required. In this context, Bayesian methods have become very popular in several fields over the last years, in order to obtain parameter inversion, model selection or uncertainty quantification. Bayesian inference requires the approximation of complicated integrals involving (often costly) posterior distributions. Generally, this approximation is obtained by means of Monte Carlo (MC) methods. In order to reduce the computational cost of the corresponding technique, surrogate models (also called emulators) are often employed. Another alternative approach is the so-called Approximate Bayesian Computation (ABC) sc…