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Found 5 papers, 5 papers with code
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
The lgpr tool is implemented as a comprehensive and user-friendly R-package.
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE).
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates.
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent.
