14 citations · 14 across the 1 of their papers we have counts for
4 papers
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…
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…
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…