1 citations · 1 across the 1 of their papers we have counts for
5 papers
Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology
Henrik Häggström, Sebastian Persson, Marija Cvijovic +1
The analysis of data from multiple experiments, such as observations of several individuals, is commonly approached using mixed-effects models, which account for variation between…
Simulation-based inference using splitting schemes for partially observed diffusions in chemical reaction networks
Petar Jovanovski, Andrew Golightly, Umberto Picchini +1
We address the problem of simulation and parameter inference for chemical reaction networks described by the chemical Langevin equation, a stochastic differential equation (SDE) re…
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah +2
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators. These approaches are collectively…
Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations
Petar Jovanovski, Andrew Golightly, Umberto Picchini
We develop a Bayesian inference method for discretely-observed stochastic differential equations (SDEs). Inference is challenging for most SDEs, due to the analytical intractabilit…
Guided sequential ABC schemes for intractable Bayesian models
Umberto Picchini, Massimiliano Tamborrino
Sequential algorithms such as sequential importance sampling (SIS) and sequential Monte Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a readily…