Papers parameter estimation
“parameter estimation” 태그가 달린 논문 1,719편 · 필터 해제
Analytic estimation of parameters of stochastic volatility diffusion models with exponential-affine characteristic function for currency option pricing
This dissertation develops and justifies a novel method for deriving approximate formulas to estimate two parameters in stochastic volatility diffusion models with exponentially-affine characteristic functions and single…
parameter estimationA Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models
Stochastic Petri Nets (SPNs) are an increasingly popular tool of choice for modeling discrete-event dynamics in areas such as epidemiology and systems biology, yet their parameter estimation remains challenging in genera…
Bayesian InferenceEpidemiologyparameter estimationRobust Spatiotemporal Epidemic Modeling with Integrated Adaptive Outlier Detection
In epidemic modeling, outliers can distort parameter estimation and ultimately lead to misguided public health decisions. Although there are existing robust methods that can mitigate this distortion, the ability to simul…
Outlier Detectionparameter estimationStructural System Identification via Validation and Adaptation
Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a ne…
parameter estimationUncertainty QuantificationIMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
Elucidating the biomechanical behavior of the myocardium is crucial for understanding cardiac physiology, but cannot be directly inferred from clinical imaging and typically requires finite element (FE) simulations. Howe…
parameter estimationSpecificityOptimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforce…
Deep Reinforcement LearningExperimental DesignModel Predictive Controlparameter estimationDynamic Hybrid Modeling: Incremental Identification and Model Predictive Control
Mathematical models are crucial for optimizing and controlling chemical processes, yet they often face significant limitations in terms of computational time, algorithm complexity, and development costs. Hybrid models, w…
Model Predictive Controlparameter estimationBayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Parameter estimation is a foundational step in statistical modeling, enabling us to extract knowledge from data and apply it effectively. Bayesian estimation of parameters incorporates prior beliefs with observed data to…
Bayesian Inferenceparameter estimationUncertainty QuantificationModeling Transmission Dynamics of Tuberculosis: Parameter Estimation and Sensitivity Analysis Using Real-World Data
Tuberculosis (TB) continues to pose a major public health challenge, particularly in high-burden regions such as Ethiopia, necessitating a more profound understanding of its transmission dynamics. In this study, we devel…
parameter estimationSensitivityAdjustment for Confounding using Pre-Trained Representations
There is growing interest in extending average treatment effect (ATE) estimation to incorporate non-tabular data, such as images and text, which may act as sources of confounding. Neglecting these effects risks biased re…
parameter estimationTransfer LearningImproved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Trajectory Errors
Summary: Errors in gradient trajectories introduce significant artifacts and distortions in magnetic resonance images, particularly in non-Cartesian imaging sequences, where imperfect gradient waveforms can greatly reduc…
Image Reconstructionparameter estimationDoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar
Sensing emerges as a critical challenge in 6G networks, which require simultaneous communication and target sensing capabilities. State-of-the-art super-resolution techniques for the direction of arrival (DoA) estimation…
parameter estimationSuper-ResolutionImaging at the quantum limit with convolutional neural networks
Deep neural networks have been shown to achieve exceptional performance for computer vision tasks like image recognition, segmentation, and reconstruction or denoising. Here, we evaluate the ultimate performance limits o…
DenoisingImage Reconstructionparameter estimationOn the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions
Solving inverse problems in cardiovascular modeling is particularly challenging due to the high computational cost of running high-fidelity simulations. In this work, we focus on Bayesian parameter estimation and explore…
AnatomyDimensionality Reductionparameter estimationBias and Identifiability in the Bounded Confidence Model
Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data…
modelparameter estimationJoint Angle and Velocity-Estimation for Target Localization in Bistatic mmWave MIMO Radar in the Presence of Clutter
Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar system in the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation…
parameter estimationSuper-ResolutionSystem Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters
Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification in dynamic systems. By leveraging the Kolmogorov-Arnold representation theorem, KANs enable fun…
Kolmogorov-Arnold Networksparameter estimationConvolutional method for data assimilation An improved method on neuronal electrophysiological data
We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging…
parameter estimationSimulation-trained conditional normalizing flows for likelihood approximation: a case study in stress regulation kinetics in yeast
Physics-inspired inference often hinges on the ability to construct a likelihood, or the probability of observing a sequence of data given a model. These likelihoods can be directly maximized for parameter estimation, in…
parameter estimationGeneralized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals
Gaussian and Laplacian entropy models are proved effective in learned point cloud attribute compression, as they assist in arithmetic coding of latents. However, we demonstrate through experiments that there is still unu…
Attributeparameter estimationVideo Compression