63 citations · 246 across the 20 of their papers we have counts for
21 papers
Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods
Umberto Picchini
We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation. Building on Gaussian mixtures-of-e…
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…
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…
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…
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
Stefan T. Radev, Marvin Schmitt, Valentin Pratz +3
This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulati…
Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality
Konstantinos Konstantinou, Farnaz Ghorbanpour, Umberto Picchini +2
Diabetic neuropathy is a disorder characterized by impaired nerve function and reduction of the number of epidermal nerve fibers per epidermal surface. Additionally, as neuropathy…
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…
Sequential Neural Posterior and Likelihood Approximation
Samuel Wiqvist, Jes Frellsen, Umberto Picchini
We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm. SNPLA is a normalizing flows-based algorithm for inference in implicit models, and ther…
Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods
Umberto Picchini, Umberto Simola, Jukka Corander
Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is analytically or computationally intractable. In SL, the likelihood function of the d…
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…
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini +1
We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-…
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…
Likelihood-free stochastic approximation EM for inference in complex models
Umberto Picchini
A maximum likelihood methodology for the parameters of models with an intractable likelihood is introduced. We produce a likelihood-free version of the stochastic approximation exp…
Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography study
Umberto Picchini, Julie Lyng Forman
We consider Bayesian inference for stochastic differential equation mixed effects models (SDEMEMs) exemplifying tumor response to treatment and regrowth in mice. We produce an exte…
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)…
Approximate maximum likelihood estimation using data-cloning ABC
Umberto Picchini, Rachele Anderson
A maximum likelihood methodology for a general class of models is presented, using an approximate Bayesian computation (ABC) approach. The typical target of ABC methods are models…
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…
Inference for SDE models via Approximate Bayesian Computation
Umberto Picchini
Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing…
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…