Approximate Bayesian Computation (ABC) provides methods for Bayesian
inference in simulation-based stochastic models which do not permit tractable
likelihoods. We present a new ABC method which uses probabilistic neural
emulator networks to learn synthetic likelihoods on simulated data -- both
local emulators which approximate the likelihood for specific observed data, as
well as global ones which are applicable to a range of data. Simulations are
chosen adaptively using an acquisition function which takes into account
uncertainty about either the posterior distribution of interest, or the
parameters of the emulator. Our approach does not rely on user-defined
rejection thresholds or distance functions. We illustrate inference with
emulator networks on synthetic examples and on a biophysical neuron model, and
show that emulators allow accurate and efficient inference even on
high-dimensional problems which are challenging for conventional ABC
approaches.

使用基于概率神经模拟器网络的近似贝叶斯计算方法，通过学习模拟数据的合成似然函数，自适应选择模拟操作，并利用获取函数对不确定性进行建模，无需事先定义拒绝阈值或距离函数，实现模拟容易应用于高维问题的准确高效的推断。

使用仿真器网络进行无似然推断

Likelihood-free inference with emulator networks

Auto-encoding generative adversarial networks (GANs) combine the standard GAN
algorithm, which discriminates between real and model-generated data, with a
reconstruction loss given by an auto-encoder. Such models aim to prevent mode
collapse in the learned generative model by ensuring that it is grounded in all
the available training data. In this paper, we develop a principle upon which
auto-encoders can be combined with generative adversarial networks by
exploiting the hierarchical structure of the generative model. The underlying
principle shows that variational inference can be used a basic tool for
learning, but with the in- tractable likelihood replaced by a synthetic
likelihood, and the unknown posterior distribution replaced by an implicit
distribution; both synthetic likelihoods and implicit posterior distributions
can be learned using discriminators. This allows us to develop a natural fusion
of variational auto-encoders and generative adversarial networks, combining the
best of both these methods. We describe a unified objective for optimization,
discuss the constraints needed to guide learning, connect to the wide range of
existing work, and use a battery of tests to systematically and quantitatively
assess the performance of our method.

本篇论文提供了一个新的原则，即利用生成模型的层次结构将自编码器与生成对抗网络相结合，以防止学习到的生成模型中的模式崩溃，并使用区分器学习合成可能性和隐式后验分布。