Guided sequential ABC schemes for intractable Bayesian models
arXiv:2206.12235 · doi:10.1214/24-BA1451
Abstract
Sequential algorithms such as sequential importance sampling (SIS) and sequential Monte Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a readily available likelihood function. For approximate Bayesian computation (ABC), SMC-ABC is the state-of-art sampler. However, since the ABC paradigm is intrinsically wasteful, sequential ABC schemes can benefit from well-targeted proposal samplers that efficiently avoid improbable parameter regions. We contribute to the ABC modeller's toolbox with novel proposal samplers that are conditional to summary statistics of the data. In a sense, the proposed parameters are "guided" to rapidly reach regions of the posterior surface that are compatible with the observed data. This speeds up the convergence of these sequential samplers, thus reducing the computational effort, while preserving the accuracy in the inference. We provide a variety of guided Gaussian and copula-based samplers for both SIS-ABC and SMC-ABC easing inference for challenging case-studies, including multimodal posteriors, highly correlated posteriors, hierarchical models with about 20 parameters, and a simulation study of cell movements using more than 400 summary statistics.
47 pages, added new case study (Lotka-Volterra), see also Table 3 in supplementary
References in corpus (15)
- Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
- Adaptive approximate Bayesian computation
- Non-linear regression models for Approximate Bayesian Computation
- Effective Sample Size for Importance Sampling based on discrepancy measures
- Flexible statistical inference for mechanistic models of neural dynamics
- Automatic Posterior Transformation for Likelihood-Free Inference
- Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
- Inference for SDE models via Approximate Bayesian Computation
- Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation
- Component-wise approximate Bayesian computation via Gibbs-like steps
- ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
- Ensemble Kalman Inversion for General Likelihoods
- Optimal proposals for Approximate Bayesian Computation
- Sequential Neural Posterior and Likelihood Approximation
- Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods