Bayesian Inference for Complex Systems

数据处理在社会中无处不在。当然，数据可能包含错误或可能只有一定程度的准确性。为了使数据处理值得信赖，量化这种不确定性是至关重要的，要了解不确定性的累积增加可以突破不确定性阈值的方式，并设计有效地将更高水平的确定性注入不确定系统的方法。这个问题的基本轮廓属于贝叶斯推理，但解决方案往往难以扩展到大型复杂系统。我们提出了一种组合的新方法，我们通过结合对较小的（因此更容易建模和推理的）子系统的推理来对大型复杂系统进行贝叶斯推理。这支持重用-例如，对系统中特定组件的小更改只需要小更改来生成修订的不确定性预测。一个关键的独特优势是我们的贝叶斯推理模型是计算性的，因为模型自动支持代码提取。这将我们的方法与其他许多在软件生产之前就停止的方法区别开来。将组合性应用于基础计算动力学是非常新颖的，并且可能产生新的不确定性结构化计算形式，从而为我们的模型和代码带来重用。最终，这将创建一个性能良好的组件库，用于构建定制系统，降低不确定性量化的成本。

Data processing is ubiquitous across society. Of course, data may contain errors or may have only a certain degree of accuracy. For data processing to be trustworthy, it is vital to quantify such uncertainties, to understand the ways in which cumulative accretion of uncertainty can breach uncertainty thresholds, and to devise ways to efficiently inject greater levels of certainty into uncertain systems. The basic contours of this problem fall within Bayesian Inference but solutions often are difficult to scale to large complex systems. We propose a new approach which is compositional in that we conduct Bayesian Inference on large complex systems by combining inference on the smaller - and hence easier to model and reason about - subsystems. This supports reuse - eg small changes to particular components in a system only require small changes to generate revised uncertainty predictions. A key distinctive advantage is that our models of Bayesian Inference are computational in that the model automatically supports code extraction. This distinguishes our approach from many others which stop before the production of software. Applying compositionality to the underpinning computational dynamics is highly novel and likely to produce new forms of structured computation of uncertainty bringing reuse to our model and our code. Ultimately, this creates a library of well-behaved components for constructing bespoke systems with lower costs for uncertainty quantification.