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Found 8 papers, 7 papers with code
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators.
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulation-based inference.
Sequential Neural Posterior and Likelihood Approximation
We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm.
Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods
Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is analytically or computationally intractable.
Methodology
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
Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography study
Results from the case study and from a simulation study show that our models are able to reproduce the observed growth patterns and that Bayesian synthetic likelihoods perform similarly to exact Bayesian inference.
Applications Computation Methodology
