Copertina

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hjort, Anders, Hermansen, Gudmund Horn, Pensar, Johan, Williams, Jonathan P.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Applications
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Conflict Forecasting via Conformal Prediction for Markov Processes
di: Basarkar, Aditya, et al.
Pubblicazione: (2026)

Recent advances in statistical methodology applied to the Hjort liver index time series (1859-2012) and associated influential factors
di: Hermansen, Gudmund H., et al.
Pubblicazione: (2026)

pintervals: an R package for model-agnostic prediction intervals
di: Randahl, David, et al.
Pubblicazione: (2026)

Partially stochastic deep learning with uncertainty quantification for model predictive heating control
di: Hannula, Emma, et al.
Pubblicazione: (2025)

Bayesian hidden Markov models for latent variable labeling assignments in conflict research: application to the role ceasefires play in conflict dynamics
di: Williams, Jonathan P, et al.
Pubblicazione: (2021)

Uncertainty quantification for data-driven weather models
di: Bülte, Christopher, et al.
Pubblicazione: (2024)

Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
di: Hermansen, Tobias Østmo, et al.
Pubblicazione: (2025)

Improving the statistical efficiency of cross-conformal prediction
di: Gasparin, Matteo, et al.
Pubblicazione: (2025)

A weighted-likelihood framework for class imbalance in Bayesian prediction models
di: Lazic, Stanley E.
Pubblicazione: (2025)

Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
di: Cheng, Si, et al.
Pubblicazione: (2024)

Surrogate modeling for uncertainty quantification in nonlinear dynamics
di: Marelli, S., et al.
Pubblicazione: (2025)

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
di: Papacharalampous, Georgia, et al.
Pubblicazione: (2023)

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
di: Kim, Jungho, et al.
Pubblicazione: (2024)

GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction
di: Lou, Xiayin, et al.
Pubblicazione: (2024)

Quantifying uncertainty in climate projections with conformal ensembles
di: Harris, Trevor, et al.
Pubblicazione: (2024)

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing
di: Sadhuka, Shuvom, et al.
Pubblicazione: (2025)

Rashomon perspective for measuring uncertainty in the survival predictive maintenance models
di: Yardimci, Yigitcan, et al.
Pubblicazione: (2025)

Density correction for multivariate spatial fields of global climate model output using deep learning
di: Majumder, Reetam, et al.
Pubblicazione: (2024)

Geographic ratemaking with spatial embeddings
di: Blier-Wong, Christopher, et al.
Pubblicazione: (2021)

Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
di: Benitez-Aurioles, Jose, et al.
Pubblicazione: (2026)

Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
di: Wessel, Jakob Benjamin, et al.
Pubblicazione: (2024)

A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling
di: Blasi, Federico, et al.
Pubblicazione: (2024)

A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data
di: Mohamed, Zeinab, et al.
Pubblicazione: (2025)

Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches
di: Lam, Ka Kin, et al.
Pubblicazione: (2021)

Uncertainty quantification of a multi-component Hall thruster model at varying facility pressures
di: Marks, Thomas A., et al.
Pubblicazione: (2025)

Bayesian calibration of stochastic agent based model via random forest
di: Robertson, Connor, et al.
Pubblicazione: (2024)

Stability of clinical prediction models developed using statistical or machine learning methods
di: Riley, Richard D, et al.
Pubblicazione: (2022)

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
di: Lipiecki, Arkadiusz, et al.
Pubblicazione: (2025)

Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1
di: Kotsuki, Shunji, et al.
Pubblicazione: (2024)

An unified approach to link prediction in collaboration networks
di: Sosa, Juan, et al.
Pubblicazione: (2024)

Causal machine learning for predicting treatment outcomes
di: Feuerriegel, Stefan, et al.
Pubblicazione: (2024)

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?
di: Edelstein, Rachel, et al.
Pubblicazione: (2024)

Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
di: Wang, Qingyi, et al.
Pubblicazione: (2023)

Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands
di: Tassopoulou, Vasiliki, et al.
Pubblicazione: (2025)

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
di: Longjohn, Rachel, et al.
Pubblicazione: (2025)

Can artificial intelligence predict clinical trial outcomes?
di: Jin, Shuyi, et al.
Pubblicazione: (2024)

Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression
di: García-Ruiz, Rubén Antonio, et al.
Pubblicazione: (2024)

ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model
di: Thapa, Krishu K, et al.
Pubblicazione: (2025)

Uncertainty-Aware Sparse Identification of Dynamical Systems via Bayesian Model Averaging
di: Kashiwamura, Shuhei, et al.
Pubblicazione: (2026)

Applications of machine learning to predict seasonal precipitation for East Africa
di: Scheuerer, Michael, et al.
Pubblicazione: (2024)