Pages that link to "Item:Q1644708"
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The following pages link to Gaussian process approximations for fast inference from infectious disease data (Q1644708):
Displaying 10 items.
- Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study (Q1647581) (← links)
- Identifying main effects and interactions among exposures using Gaussian processes (Q2078748) (← links)
- Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics (Q2081400) (← links)
- A martingale formulation for stochastic compartmental susceptible-infected-recovered (SIR) models to analyze finite size effects in COVID-19 case studies (Q2086995) (← links)
- Inference for partially observed epidemic dynamics guided by Kalman filtering techniques (Q2242184) (← links)
- Fitting stochastic epidemic models to gene genealogies using linear noise approximation (Q2686013) (← links)
- GAUSSIAN PROCESS MODELS FOR MORTALITY RATES AND IMPROVEMENT FACTORS (Q4691257) (← links)
- A scalable gaussian process analysis algorithm for biomass monitoring (Q4969794) (← links)
- Gaussian process emulators for spatial individual‐level models of infectious disease (Q5507371) (← links)
- Accelerating Bayesian inference for stochastic epidemic models using incidence data (Q6089189) (← links)
