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aggregation process in parameter estimation

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aggregation process in parameter estimation

Evaluation of Parameter Estimation Methods for Crystallization

To establish a process model, parameter estimation (PE) is applied to determine an optimal set of parameters by minimizing the sum of squared errors between the experimental results and the model output. ... Parameters of the aggregation kernel could be determined accurately assuming exact data of the CSD. Again, more …

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A parameter estimation method based on aggregated data

PARAMETER ESTIMATION METHOD 207 input and output data, but the parameter estimation retains its effective- ness. 6. CONCLUSIONS A new parameter …

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Model aggregation techniques in federated learning: A …

During the aggregation process, the parameters of each client are weighted and averaged to produce a global model, where the weight factor is the proportion of the …

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Cost aggregation benchmark for light field depth estimation

The mean value of average weighted rank for other cost aggregation methods is shown in Table 4.Both non-local and segment-tree based cost aggregation methods have similar optimal parameters (σ), which are 0.02 and 0.05.The optimal parameters are small because both methods depend on the color similarity only.

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[PDF] Optimal Parameter Estimation of Conceptually-Based …

Using these models, the possible benefits of data aggregation with regards to parameter estimation are investigated by means of a simulation study. The application made with reference to the ARMA(1,1) model shows advantageous effects of data aggregation, while the same benefits are not found for estimation of the conceptual parameters with the ...

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Parameter Estimation of Binned Hawkes Processes

Here we consider parameter estimation of the Hawkes process, a type of self-exciting point process that has found application in the modeling of financial stock markets, earthquakes and social media cascades. ... Here we use "binned" to mean an aggregation of the latent continuous time process into a series of counts per interval of time.

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Adjustable discretized population balance equations: …

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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Feature aggregation and modulation network for single …

Deep learning-based methods have recently achieved satisfying results in image dehazing. However, we observe that various researchers devote themselves to learning haze-free images directly, while often paying no attention to the physical features of the hazy image formation process. For single image dehazing, a suitable transmission …

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9.1 Estimating Costs – Project Management from …

An estimate that is obtained by scaling up an estimate from a similar project is a(n) _____ estimate. An estimate that uses standard costs per unit such as price per square foot or price per cubic yard is a _____ …

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A data assimilation approach for groundwater parameter estimation …

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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GHG Protocol guidance on uncertainty assessment in …

Short Guidance for Calculating Measurement and Estimation Uncertainty for GHG Emissions other parameters) used as inputs in an emission estimation model. Two types of parameter uncertainties can be identified in this context: systematic and statistical uncertainties. Systematic uncertainty occurs if data are systematically biased. In other ...

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Propagation Channel Characterization, Parameter Estimation…

A comprehensive reference giving a thorough explanation of propagation mechanisms, channel characteristics results, measurement approaches and the modelling of channels Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular …

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Sparse estimation of parameter support sets for …

Sparse estimation of parameter support sets ... in the exponential family in which the parameter is a function of the process history H t ... In this work we propose a sparse estimation method based on simple aggregation oper-ations applied to multiple estimates obtained from data resampling, and demonstrate the ...

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Chapter 4 Parameter Estimation

English parameter q differs from π), because it ignores the data completely. Consistency is nearly always a desirable property for a statistical estimator. 4.2.2 Bias If we view the collection (or sampling) of data from which to estimate a population pa …

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The effect of temporal aggregation on parameter estimation …

This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.

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Sparse estimation of parameter support sets for …

resampling and model aggregation Trevor D. Ruiz∗1, Sharmodeep Bhattacharyya2, ... Abstract The central problem we address in this work is estimation of the parameter …

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Using Bayesian parameter estimation to learn more from …

The Bayesian approach. The most common approach to parameter estimation is to frame it as an optimization problem over the parameters, with the goal of mini-mizing some …

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Bayesian inference for aggregated Hawkes processes

quakes, social networks and stock markets. The established estimation process requires that researchers have access to the exact time stamps and marks. However, available data are often rounded or aggregated. We develop a Bayesian estimation procedure for the parameters of a Hawkes process based on aggre-gated data.

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Kinetics of protein aggregation. Quantitative estimation of …

The model of protein refolding explaining such a kinetic regularity has been proposed. When aggregation of protein substrate follows first order kinetics, parameters A(lim) and kI may be used for the quantitative characterization of the chaperone-like activity in the test-systems based on suppression of protein aggregation.

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GHG Protocol guidance on uncertainty assessment in …

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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Parameters Estimation of a Lotka-Volterra Model in an …

the KF approach mentioned above. The parameter estimation problem for economical models has been studied by many scientists, Yu and Phillips [10] utilized a Gaussian method to estimate the parameters of continuous time short-term interest rate models. Faff and Gray [11] considered the estimation of Proceedings of the of the 17th Conference …

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Learning interacting particle systems: Diffusion parameter estimation

In this paper, we study the parameter estimation of interacting particle systems subject to the Newtonian aggregation and Brownian diffusion. Specifically, we construct an estimator ν̂ with partial...

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Robust Aggregation for Federated Learning

Abstract: We present a novel approach to federated learning that endows its aggregation process with greater robustness to potential poisoning of local data or model parameters of participating devices. The proposed approach, Robust Federated Aggregation (RFA), relies on the aggregation of updates using the geometric median, which can be …

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Learning interacting particle systems: Diffusion parameter estimation

PARAMETER AND STATE ESTIMATION FOR A DIFFUSION PROCESS**This research was supported by the U.S. Army Research Office under ARO grant DAAG-29-77-G-0061. 1978 • ... Learning interacting particle systems: diffusion parameter estimation for aggregation equations Hui Huang∗, Jian-Guo Liu†, Jianfeng Lu‡ arXiv:1802.02267v1 …

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Estimation of aggregation kernels based on Laurent …

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 …

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Examining the impacts of crash data aggregation on SPF estimation

Aggregation shown to produce inaccurate estimates of overdispersion parameter than using disaggregated data. • Differences in overdispersion parameter estimation leads to bias in outcomes of Empirical Bayes adjustments. • Research suggests SPFs should aggregate data to level that will be used for crash frequency predictions.

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A parameter estimation method for multivariate binned …

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 …

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A Parameter Estimation Method for Multivariate Aggregated …

A Parameter Estimation Method for Multivariate Aggregated Hawkes Processes. It is often assumed that events cannot occur simultaneously when modelling …

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Kinetics of Protein Aggregation. Quantitative Estimation of …

The experimental data on the kinetics of irreversible aggregation of proteins caused by exposure to elevated temperatures or the action of denaturing agents (guanidine hydrochloride, urea) have been analyzed. It was shown that the terminal phase of aggregation followed, as a rule, first order kinetics. For the kinetic curves registered by …

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Multi‐scale homography estimation based on dual feature aggregation …

Second, each pair of feature maps are input into the corresponding predictors: The Contextual Correlation module and two DFA-T modules. In other words, the prediction of parameters by the network is performed simultaneously at three different scales. We describe the forward process as pseudo-code, as shown in Algorithm 1. For …

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