Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
About
We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible. SNL trains an autoregressive flow on simulated data in order to learn a model of the likelihood in the region of high posterior density. A sequential training procedure guides simulations and reduces simulation cost by orders of magnitude. We show that SNL is more robust, more accurate and requires less tuning than related neural-based methods, and we discuss diagnostics for assessing calibration, convergence and goodness-of-fit.
George Papamakarios, David C. Sterratt, Iain Murray• 2018
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Simulation-Based Inference | Hierarchical Two Moons | l-C2ST0.241 | 55 | |
| Simulation-Based Inference | Hierarchical Gaussian Linear | l-C2ST0.001 | 55 | |
| Simulation-Based Inference | Hierarchical Gaussian Linear Uniform | l-C2ST0.0017 | 55 | |
| Simulation-Based Inference | Hierarchical Gaussian Mixture | l-C2ST0.0587 | 55 | |
| Simulation-Based Inference | Hierarchical SLCP | l-C2ST23.6 | 54 | |
| Simulation-Based Inference | Hierarchical SIR | l-C2ST5.73e-4 | 53 | |
| Bayesian Inference | Bayesian Inverse Problems Task 5 1.0 | C2ST66.8 | 17 | |
| Bayesian Inference | Bayesian Inverse Problems Task 4 1.0 | C2ST73.1 | 17 | |
| Bayesian Inference | Bayesian Inverse Problems Task 1 1.0 | C2ST51.9 | 17 | |
| Bayesian Inference | Bayesian Inverse Problems Task 3 1.0 | C2ST69.9 | 17 |
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