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Found 19 papers, 10 papers with code
The Polynomial Stein Discrepancy for Assessing Moment Convergence
We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference.
The Statistical Accuracy of Neural Posterior and Likelihood Estimation
Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex modeling scenarios, and in situations where conducting amortized inference is a necessity.
A Comprehensive Guide to Simulation-based Inference in Computational Biology
In this paper, we provide comprehensive guidelines for deciding between SBI approaches for complex biological models.
Ecosystem knowledge should replace coexistence and stability assumptions in ecological network modelling
Ecosystem models are often constructed by assuming stability and coexistence in ecological communities as a proxy for abundance data when monitoring programs are not available.
Preconditioned Neural Posterior Estimation for Likelihood-free Inference
To overcome this, we propose preconditioned NPE (PNPE) and its sequential version (PSNPE), which uses a short run of ABC to effectively eliminate regions of parameter space that produce large discrepancy between simulations and data and allow the posterior emulator to be more accurately trained.
Unlocking ensemble ecosystem modelling for large and complex networks
Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles.
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
We combine the Wasserstein Gaussianization transformation with robust BSL, and an efficient Variational Bayes procedure for posterior approximation, to develop a highly efficient and reliable approximate Bayesian inference method for likelihood-free problems.
Bayesian score calibration for approximate models
In this paper we propose a new method for adjusting approximate posterior samples to reduce bias and produce more accurate uncertainty quantification.
Transport Reversible Jump Proposals
Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications.
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localisation
We describe an idealized low-dimensional summary statistic that is, in principle, suitable for marginal estimation.
Inference of ventricular activation properties from non-invasive electrocardiography
We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites).
Accelerating sequential Monte Carlo with surrogate likelihoods
Delayed-acceptance is a technique for reducing computational effort for Bayesian models with expensive likelihoods.
Computation Methodology
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
We address this gap in the literature by devising a novel sequential design framework for parameter estimation that uses the Mutual Information (MI) between model parameters and simulated data as a utility function to find optimal experimental designs, which has not been done before for implicit models.
Efficient Bayesian synthetic likelihood with whitening transformations
Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statistic with a known, tractable distribution -- typically Gaussian -- and then performs statistical inference using standard likelihood-based techniques.
BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation.
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters.
Computation Methodology
Robust Approximate Bayesian Inference with Synthetic Likelihood
Similar to other approximate Bayesian methods, such as the method of approximate Bayesian computation, implicit in the application of BSL is the maintained assumption that the data generating process (DGP) can generate simulated summary statistics that capture the behaviour of the observed summary statistics.
Methodology Applications Computation
Vector operations for accelerating expensive Bayesian computations -- a tutorial guide
We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments using standard tools.
Regularized Zero-Variance Control Variates
Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target.
Computation Methodology
