Variational methods for simulation-based inference

03/08/2022
by   Manuel Glöckler, et al.
13

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.

READ FULL TEXT

page 6

page 8

page 17

page 25

research
11/10/2018

Bayesian variational inference for exponential random graph models

Bayesian inference for exponential random graphs (ERGMs) is a doubly int...
research
04/21/2018

Variational Inference In Pachinko Allocation Machines

The Pachinko Allocation Machine (PAM) is a deep topic model that allows ...
research
03/29/2016

Submodular Variational Inference for Network Reconstruction

In real-world and online social networks, individuals receive and transm...
research
10/04/2022

Amortized Bayesian Inference of GISAXS Data with Normalizing Flows

Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imag...
research
10/26/2022

Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference

We introduce two synthetic likelihood methods for Simulation-Based Infer...
research
10/10/2022

Truncated proposals for scalable and hassle-free simulation-based inference

Simulation-based inference (SBI) solves statistical inverse problems by ...
research
11/27/2020

Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

We present algorithms (a) for nested neural likelihood-to-evidence ratio...

Please sign up or login with your details

Forgot password? Click here to reset