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Found 4 papers, 4 papers with code
Sequential Neural Posterior and Likelihood Approximation
We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm.
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms
Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dynamics, as well as the variability between experimental units and, optionally, account for measurement error.
Computation Methodology
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries.
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
In our accelerated algorithm the calculations in the "second stage" of the delayed-acceptance scheme are reordered in such as way that we can obtain a significant speed-up in the MCMC sampling, when the evaluation of the likelihood function is computationally intensive.
Computation
