63 citations · 246 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
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…
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms
Samuel Wiqvist, Andrew Golightly, Ashleigh T. McLean +1
Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dyn…
Stratified sampling and bootstrapping for approximate Bayesian computation
Umberto Picchini, Richard G. Everitt
Approximate Bayesian computation (ABC) is computationally intensive for complex model simulators. To exploit expensive simulations, data-resampling via bootstrapping can be employe…
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
Samuel Wiqvist, Umberto Picchini, Julie Lyng Forman +2
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution via a two-stages version of the Metropolis-Hastings algorithm, by combining the target…
Coupling stochastic EM and Approximate Bayesian Computation for parameter inference in state-space models
Umberto Picchini, Adeline Samson
We study the class of state-space models and perform maximum likelihood estimation for the model parameters. We consider a stochastic approximation expectation-maximization (SAEM)…
Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation
Umberto Picchini, Julie Lyng Forman
In recent years dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However it is often computationally unfeasible to ap…
Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models
Umberto Picchini, Susanne Ditlevsen
Stochastic differential equations (SDEs) are established tools to model physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE intrinsic…