68 citations · 101 across the 3 of their papers we have counts for
4 papers
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi +2
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distr…
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Markus Heinonen, Maria Osmala, Henrik Mannerström +4
Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometr…
Learning unknown ODE models with Gaussian processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström +2
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to…
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu +2
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…