Variational methods for simulation-based inference
We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods. SNVI combines likelihood-estimation (or likelihood-ratio-estimation) with variational inference to achieve a scalable simulation-based inference approach. SNVI maintains the flexibility of likelihood(-ratio) estimation to allow arbitrary proposals for simulations, while simultaneously providing a functional estimate of the posterior distribution without requiring MCMC sampling. We present several variants of SNVI and demonstrate that they are substantially more computationally efficient than previous algorithms, without loss of accuracy on benchmark tasks. We apply SNVI to a neuroscience model of the pyloric network in the crab and demonstrate that it can infer the posterior distribution with one order of magnitude fewer simulations than previously reported. SNVI vastly reduces the computational cost of simulation-based inference while maintaining accuracy and flexibility, making it possible to tackle problems that were previously inaccessible.
Code (1)
Tasks
Bayesian InferenceVariational InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Misspecification-robust amortised simulation-based inference using variational methods
Recent advances in neural density estimation have enabled powerful simulation-based inference (SBI) methods that can flexibly approximate Bayesian inference for intractable stochastic models. Although these methods have …
Density EstimationBayesian InferenceVariational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC). In recent years, however, several methods have been propose…
Bayesian InferenceComputational EfficiencyAn Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations
It is important to estimate the errors of probabilistic inference algorithms. Existing diagnostics for Markov chain Monte Carlo methods assume inference is asymptotically exact, and are not appropriate for approximate me…
DiagnosticVariational InferenceRecursive Monte Carlo and Variational Inference with Auxiliary Variables
A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of proposal distributions and variational fami…
AstronomyStochastic OptimizationVariational InferenceVariational Autoencoders for Efficient Simulation-Based Inference
We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently …
Computational EfficiencyVariational Inference