63 citations · 246 across the 20 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
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…