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.

翻译：我们提出了序列神经变异推断(SNVI),这是一种在具有难测可能性的模型中进行巴伊西亚推断的方法。SNVI将概率估计(或概率-比率-估计)与可变推断结合起来,以实现可缩放的模拟推断法。SNVI保持了可能性(比率)估计的灵活性,允许对模拟提出任意的建议,同时在不需要MCMC取样的情况下对后方分布进行功能估计。我们提出了若干种SNVI变种,并表明这些变种在计算上比以前的算法效率高得多,在基准任务方面没有丧失准确性。我们将SNVI应用于螃蟹热点网络的神经科学模型,并表明它可以用比以前报告的少一个数量级的模拟来推断后方分布。SNVI大大降低了模拟推断的计算成本,同时保持准确性和灵活性,从而有可能解决以前无法获取的问题。