Papers parameter estimation
“parameter estimation” 태그가 달린 논문 1,719편 · 필터 해제
Standard LSParameter Estimators Ensure Finite Convergence Time for Linear Regression Equations Under an Interval Excitation Assumption
In this brief note we recall the little-known fact that, for linear regression equations (LRE) with intervally excited (IE) regressors, standard Least Square (LS) parameter estimators ensure finite convergence time (FCT)…
parameter estimationDouble Low-Rank 4D Tensor Decomposition for Circular RIS-Aided mmWave MIMO-NOMA System Channel Estimation in Mobility Scenarios
Channel estimation is not only essential to highly reliable data transmission and massive device access but also an important component of the integrated sensing and communication (ISAC) in the sixth-generation (6G) mobi…
Integrated sensing and communicationISACparameter estimationTensor DecompositionCommSense: A Rapid and Accurate ISAC Paradigm
Future 6G networks envisions to blur the line between communication and sensing, leveraging ubiquitous OFDM waveforms for both high throughput data and environmental awareness. In this work, we do a thorough analysis of …
Integrated sensing and communicationISACparameter estimationOn the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)
This paper introduces a hypothetical hybrid control framework for port-Hamiltonian (p$\mathcal{H}$) systems, employing a dynamic decomposition based on Data-Assisted Control (DAC). The system's evolution is split into tw…
parameter estimationReinforcement Learning (RL)On Inverse Problems, Parameter Estimation, and Domain Generalization
Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversion of measurements has become a popular s…
DeblurringDomain GeneralizationImage Deblurringparameter estimationProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
Neural rendering has made significant strides in 3D reconstruction and novel view synthesis. With the integration with physics, it opens up new applications. The inverse problem of estimating physics from visual data, ho…
3D ReconstructionNeRFNeural RenderingNovel View Synthesis+2Testing Hypotheses of Covariate Effects on Topics of Discourse
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying pr…
parameter estimationUncertainty QuantificationIntLevPy: A Python library to classify and model intermittent and Lévy processes
IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and L\'evy processes. The package includes functionalities for process simulation…
parameter estimationHybrid SIS Dynamics for Demand Modeling of Frequently Updated Products
We propose a hybrid spreading process model to capture the dynamics of demand for software-based products. We introduce discontinuous jumps in the state to model sudden surges in demand that can be seen immediately after…
parameter estimationBi-Level optimization for parameter estimation of differential equations using interpolation
Inverse problem or parameter estimation of ordinary differential equations is a process of obtaining the best parameters using experimental measurements of the states. Single (Multiple)-shooting is a type of sequential o…
Model Discoveryparameter estimationA condensing approach to multiple shooting neural ordinary differential equation
Multiple-shooting is a parameter estimation approach for ordinary differential equations. In this approach, the trajectory is broken into small intervals, each of which can be integrated independently. Equality constrain…
parameter estimationGenerator Based Inference (GBI)
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of the underlying theory. Modern machine l…
Anomaly Detectionparameter estimationA novel sensitivity analysis method for agent-based models stratifies in-silico tumor spheroid simulations
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact…
parameter estimationSensitivityInterturn Fault Detection in IPMSMs: Two Adaptive Observer-based Solutions
In this paper we address the problem of online detection of inter-turn short-circuit faults (ITSCFs) that occur in permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: (i) a very simple …
Fault Detectionparameter estimationEfficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing
Bayesian network classifiers (BNCs) possess a number of properties desirable for a modern classifier: They are easily interpretable, highly scalable, and offer adaptable complexity. However, traditional methods for learn…
parameter estimationLearning Parametric Distributions from Samples and Preferences
Recent advances in language modeling have underscored the role of preference feedback in enhancing model performance. This paper investigates the conditions under which preference feedback improves parameter estimation i…
Language ModelingLanguage Modellingparameter estimationSpectral clustering for dependent community Hawkes process models of temporal networks
Temporal networks observed continuously over time through timestamped relational events data are commonly encountered in application settings including online social media communications, financial transactions, and inte…
parameter estimationStochastic Block ModelPlug-and-Play Posterior Sampling for Blind Inverse Problems
We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are unknown. Unlike conventional methods th…
Blind Image DeblurringDeblurringDenoisingImage Deblurring+1Transfer learning for multifidelity simulation-based inference in cosmology
Simulation-based inference (SBI) enables cosmological parameter estimation when closed-form likelihoods or models are unavailable. However, SBI relies on machine learning for neural compression and density estimation. Th…
Density Estimationparameter estimationTransfer LearningTabPFN: One Model to Rule Them All?
Hollmann et al. (Nature 637 (2025) 319-326) recently introduced TabPFN, a transformer-based deep learning model for regression and classification on tabular data, which they claim "outperforms all previous methods on dat…
AllBayesian InferenceDensity EstimationHeterogeneous Treatment Effect Estimation+2