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In constructing multiprocessor-based distributed process control systems, one approach is to use low-end processors to carry out direct control tasks …
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larization hyperparameters by choosing parameters having a maximum selection frequency above a certain threshold. Ruiz et al. [2020] considered estimating VAR models by pooling supports meeting optimality criteria across resamples. In this work we propose a sparse estimation method based on simple aggregation oper-
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this: (1) specify a probabilistic model that has parameters. (2) Learn the value of those parameters from data. Parameters Before we dive into parameter estimation, first let's revisit the concept of parameters. Given a model, the parameters are the numbers that yield the actual distribution. In the case of a Bernoulli random variable,
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After each of the quality parameters was converted into the subindex value, the subindex values of the selected of the quality parameters were combined into a numerical value that indicated the level of fruit quality. In this study, two mathematical equations were used for the aggregation process. 2.4.3.1. Fruit quality index 1 (FQI1)
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The techniques used for parameter estimation are called estimators. Rank Regression (Least Squares): A method of finding parameter values that minimizes the sum of the squares of the residuals. Maximum Likelihood Estimation: A method of finding parameter values that, given a set of observations, will maximize the likelihood function.
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The dynamics of the aggregation process described by Eq. (3) are governed by the aggregation kernel k, which is assumed to be independent of time.The aim of this work is the estimation of this kernel. For developing and assessing the estimation procedure described below, we use three different kernel functions which are given in …
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inventory is the uncertainties associated with parameters (e.g. activity data, emission factors, and 3 The role of expert judgment in the assessment of the parameter can be twofold: Firstly, expert judgment can be the source of the data that are necessary to estimate the parameter. Secondly, expert judgment can help (in combination with
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where T is the maximum observation or simulation time, (t^p_l) is the lth event in process p, and (N^{(p)}(T)) is the total number of events in process p.When we observe an aggregation of these latent continuous times to a count process of events per time bin, we lose information and in particular the likelihood of our observed event times …
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The latter two approaches are described in 2.1 Joint Estimation of Multiple Precision matrices, 2.2 Regularized aggregation, respectively, and our suggested method is illustrated in Section 2.3. MRI methods were reported in Pierce and McDowell (2017) and described in Section 2.4. 2.1.
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There parameter estimates suggest that for each event in process 1, an average of 0.27 ev ents will be triggered in process 2. The baseline parameters given by ν indicate the rate of the events ...
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Spatial heterogeneity in groundwater system introduces significant challenges in groundwater modeling and parameter calibration. In order to mitigate the modeling uncertainty, data assiilation methods have been applied in the parameter estimation by assessing the uncertainties from both groundwater model and …
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The main benefit of cost aggregation is that it allows the project management team to see scheduled spending for every time period. This will allow project managers to see the activities as well as the corresponding costs. The main output of cost aggregate is the determination of the cost-performance baseline.
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Aggregation parameter A lot of substances and components are present in wastewaters and can be measured, especially the ... (/V-methylpyrolidone) and nitrite can be detected in effluents or process water [21], Moreover, the estimation of complementary aggregate parameters, such as total oxygen demand (TOD), is possible from the estimation of ...
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From Tables 2 and 3, it can be observed that the parameters estimation of μ λ and σ B 2 calculated by the direct MLE method and the unbiased parameters estimation method are the same. In most of cases, the MSEs of the unbiased estimators of σ λ 2 are a little worse than the traditional MLE method, especially for small n.This phenomenon is in …
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A tool to quantify the pollution potential of leachate, termed the revised leachate pollution index (r-LPI), has been developed. It was developed using the fuzzy Delphi analytic hierarchy process (FDAHP). The formulation entails four major steps: parameter selection, weight calculation, normalization of parameters, and aggregation …
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The econometrics of aggregation is about modelling the relationship between individual (micro) behaviour and aggregate (macro) statistics, so that data …
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Table 1. Computational methods to predict protein aggregation. Predicts the formation of strand-pairs into complete β-sheets based on a free-energy model accounting for amino acid sidechain stacking contributions, entropic estimation, and steric restrictions for amyloidal parallel β-sheet formation.
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Here we generalise existing methodology on parameter estimation of univariate aggregated Hawkes processes to the multivariate case using a Monte Carlo …
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Abstract. The econometrics of aggregation is about modelling the relationship between individual (micro) behaviour and aggregate (macro) statistics, so that data from both levels can be used for estimation and inference about economic parameters. Practical models must address three types of individual heterogeneity – in …
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(1) The data aggregation scheme is designed to comprehensively utilize the measurements. (2) The specific aggregation and fusion operations are selected automatically, which avoids the feature engineering. (3) Only partial charging data is adopted, which can be applied under incomplete charging process.
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Learning interacting particle systems: diffusion parameter estimation for aggregation equations. In this article, we study the parameter estimation of interacting particle systems subject to the Newtonian aggregation. Specifically, we construct an estimator $widehat {nu}$ with partial observed data to approximate the diffusion parameter $nu ...
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The estimation result with the additional parameter for Case 3 is shown in Fig. 9, with the ground truth and the original estimation. The result demonstrated that the additional latent parameter ...
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Considering fractal aggregation and break-up, two major parameters were found to be collision efficiency α of 0.3938 and aggregate break-up coefficient K B of 4.4105 using a parameter estimation scheme coupled with an improved discretized population balance equation. This parameter estimation scheme was able to compute the …
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The overwhelming majority of studies dealing with determination of aggregation parameters from experimental titration curves, do not bother with optimal experiment design. As a consequence, the experimental setup is typically determined by other factors than the required optimum for the most accurate estimation of the search …
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the aggregation process. There is an old fable about buying eggs at 10¢ each and selling them for $1.00 per dozen, making up the difference by doing high volume. The …
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In Tiao and Wei (1976) the authors have considered the exact relationship between a given basic infinite distributed lag model and the corresponding model for temporal aggregates. In this paper we study the effect of temporal aggregation on parameter estimation in the above general finite distributed lag model (1.1).
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As a support to this procedure, Claps and Murrone [1994] reported, in even simpler systems, positive effects of data aggregation with regard to parameter estimation of ARMA models. Aggregation was ...
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Moreover, the cross-resolution parameter estimation (CRPE) is introduced into the proposed framework to enhance the flow of information and further improve the rate-distortion performance. An additional information-fidelity loss is proposed to the total loss function to adjust the contribution of the LR part to the final bit stream.
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In the present study, we developed a mathematical model of reversible platelet aggregation which incorporated a novel mechanism of disaggregation, could be used for clinical parameter estimation ...
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Suppose we want as output the average moisture content at wilting point for 1 ha squares. Whether we first aggregate the point support inputs to the 1 ha support (Fig. 4) or first run the model at all points in the area and next aggregate the point support outputs to the 1 ha support, the result will be exactly the same (Fig. 5).In other words, although …
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