Estimation theory ≈ Estimation theory
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A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis Open
A new method to estimate the trajectories of particle motion and the amount of cumulative beam damage in electron cryo-microscopy (cryo-EM) single-particle analysis is presented. The motion within the sample is modelled through the use of …
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Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique Open
Battery equivalent circuit models (ECMs) are widely employed in online battery management applications. The model parameters are known to vary according to the operating conditions, such as the battery state of charge (SOC). Therefore, onl…
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Multivariate <span>C</span>opula <span>A</span>nalysis <span>T</span>oolbox (MvCAT): Describing dependence and underlying uncertainty using a <span>B</span>ayesian framework Open
We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual‐based Gaussian likelihood func…
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Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue Open
The ultimate promise of diffusion MRI (dMRI) models is specificity to neuronal microstructure, which may lead to distinct clinical biomarkers using noninvasive imaging. While multi‐compartment models are a common approach to interpret wate…
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Compatibility in multiparameter quantum metrology Open
Simultaneous estimation of multiple parameters in quantum metrological models\nis complicated by factors relating to the (i) existence of a single probe state\nallowing for optimal sensitivity for all parameters of interest, (ii) existence…
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Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization Open
A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell’s reflection law. However, th…
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Structural Identifiability of Dynamic Systems Biology Models Open
A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determi…
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Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems Open
Summary: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction n…
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SPOTting Model Parameters Using a Ready-Made Python Package Open
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive …
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Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information Open
This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the …
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Multi-parameter quantum metrology Open
The simultaneous quantum estimation of multiple parameters can provide a\nbetter precision than estimating them individually. This is an effect that is\nimpossible classically. We review the rich background of multi-parameter\nquantum metr…
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Multipath Assisted Positioning with Simultaneous Localization and Mapping Open
This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath compone…
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Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling Open
Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and th…
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MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information Open
Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantia…
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A parameter extraction technique exploiting intrinsic properties of solar cells Open
This paper presents a parameter extraction technique for the five-parameter solar-cell model. It only requires the priori knowledge of three load points: the open circuit, the short circuit, and the maximum power points. An intrinsic prope…
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Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis Open
Chemical kinetic modeling in heterogeneous catalysis is gaining ground in its ability to provide qualitatively or even quantitatively accurate prediction of real-world behavior because of new advances in the physical and chemical represent…
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Fast likelihood-free cosmology with neural density estimators and active learning Open
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be successfully included in the simulations. …
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Efficient Phase Estimation for Interferogram Stacks Open
Signal decorrelation poses a limitation to multipass SAR interferometry. In pursuit of overcoming this limitation to achieve high-precision deformation estimates, different techniques have been developed; with SBAS, SqueeSAR and CAESAR as …
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Sample Size Requirements for Estimation of Item Parameters in the Multidimensional Graded Response Model Open
Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item respo…
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A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models Open
Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed …
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A mixture of generalized hyperbolic distributions Open
We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives within which the mixture of multivariate t ‐distributions and the mixture …
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Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes Based on KL Decomposition, MLP and LSTM Network Open
Estimation of absolute temperature distributions is crucial for many thermal processes in the nonlinear distributed parameter systems, such as predicting the curing temperature distribution of the chip, the temperature distribution of the …
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Robust and efficient parameter estimation in dynamic models of biological systems Open
Here we provide a parameter estimation strategy which combines efficient global optimization with a regularization scheme. This method is able to calibrate dynamic models in an efficient and robust way, effectively fighting overfitting and…
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The parameter estimation algorithms based on the dynamical response measurement data Open
This article studies the parameter estimation to the system response from the discrete measurement data. By constructing the dynamical rolling cost functions and using the nonlinear optimization, the gradient identification method is prese…
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Interval Estimation Methods for Discrete-Time Linear Time-Invariant Systems Open
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Online Parameter Estimation for Permanent Magnet Synchronous Machines: An Overview Open
Online parameter estimation of permanent magnet synchronous machines is critical for improving their control performance and operational reliability. This paper provides an overview of the recent achievements of online parameter estimation…
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Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks Open
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold fo…
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Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental Findings and Applications Open
Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. The common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethor…
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Computationally Efficient Composite Likelihood Statistics for Demographic Inference Open
Many population genetics tools employ composite likelihoods, because fully modeling genomic linkage is challenging. But traditional approaches to estimating parameter uncertainties and performing model selection require full likelihoods, s…
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Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty Open
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, th…