Pages that link to "Item:Q5965041"

The following pages link to Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm (Q5965041):

Displaying 31 items.

• Space-time inhomogeneous background intensity estimators for semi-parametric space-time self-exciting point process models (Q778876) (← links)
• Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data (Q830598) (← links)
• Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology (Q1622175) (← links)
• A review of self-exciting spatio-temporal point processes and their applications (Q1630387) (← links)
• sppmix: Poisson point process modeling using normal mixture models (Q1729311) (← links)
• A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal data (Q2075448) (← links)
• Local spatial log-Gaussian Cox processes for seismic data (Q2106831) (← links)
• Spatial Cox processes in an infinite-dimensional framework (Q2125481) (← links)
• Combining heterogeneous spatial datasets with process-based spatial fusion models: a unifying framework (Q2242020) (← links)
• Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ ``Real-time crime forecasting challenge'' (Q2291539) (← links)
• Spatiotemporal point processes: regression, model specifications and future directions (Q2330482) (← links)
• Spatiotemporal prediction for log-Gaussian Cox processes (Q2773211) (← links)
• Log-Gaussian Cox processes in infinite-dimensional spaces (Q4606866) (← links)
• Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes (Q5222497) (← links)
• Scalable inference for space‐time Gaussian Cox processes (Q5377195) (← links)
• Corrigendum: Spatiotemporal Prediction for Log-Gaussian Cox Processes (Q5743166) (← links)
• Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm (Q5965041) (← links)
• Multivariate geometric anisotropic Cox processes (Q6049801) (← links)
• Conditional intensity: A powerful tool for modelling and analysing point process data (Q6075101) (← links)
• Approximate Bayesian inference for multivariate point pattern analysis in disease mapping (Q6091727) (← links)
• A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes (Q6100557) (← links)
• A randomized multi-index sequential Monte Carlo method (Q6172156) (← links)
• A log-Gaussian Cox process with sequential Monte Carlo for line narrowing in spectroscopy (Q6194412) (← links)
• Integrating machine learning and Bayesian nonparametrics for flexible modeling of point pattern data (Q6554234) (← links)
• A spatio-temporal Dirichlet process mixture model for coronavirus disease-19 (Q6560559) (← links)
• Minimum contrast for the first-order intensity estimation of spatial and spatio-temporal point processes (Q6581348) (← links)
• Adaptive parameters tuning based on energy-preserving splitting integration for Hamiltonian Monte Carlo method (Q6591841) (← links)
• Generalized functional linear model with a point process predictor (Q6618446) (← links)
• A spatially discrete approximation to log-Gaussian Cox processes for modelling aggregated disease count data (Q6628748) (← links)
• A Scalable Gaussian Process for Large-Scale Periodic Data (Q6631142) (← links)
• Point process modeling through a mixture of homogeneous and self-exciting processes (Q6668594) (← links)