Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardware continues to grow, the computational requirements for the simulation of models are growing even faster. This is largely due to the increase in model complexity, often including stochastic dynamics, that is necessary to describe and characterize phenomena observed using modern, high resolution, experimental techniques. Such models are rarely analytically tractable, meaning that extremely large numbers of stochastic simulations are required for parameter inference. In such cases, parameter inference can be practically impossible. In this work, we present new computational Bayesian techniques that accelerate inference for expensive stochastic models by using computationally inexpensive approximations to inform feasible regions in parameter space, and through learning transforms that adjust the biased approximate inferences to closer represent the correct inferences under the expensive stochastic model. Using topical examples from ecology and cell biology, we demonstrate a speed improvement of an order of magnitude without any loss in accuracy. This represents a substantial improvement over current state-of-the-art methods for Bayesian computations when appropriate model approximations are available.
翻译:几乎所有科学领域都依靠统计推论来估计理论和计算模型中的未知参数。现代计算机硬件的性能在继续增长,而模拟模型的计算要求却在增长,甚至更快。这主要是由于模型复杂性增加,往往包括随机动态,而模型复杂性增加,这是描述和描述使用现代高分辨率、高分辨率、实验技术观察到的现象所必需的。这些模型在分析上很少具有可分析性,这意味着参数推导需要大量的随机模拟。在这种情况下,参数推论实际上不可能。在这项工作中,我们提出了新的计算方法,通过使用计算成本低的近似值来告知参数空间的可行区域,并通过学习改变,调整偏差的推论,以更近地代表昂贵的随机推论模型下的正确推论。我们用生态和细胞生物学的特有实例,表明在不造成任何准确性损失的情况下,一个数量级的快速改进。这代表了目前巴伊斯计算模型的状态方法有了重大改进。