Gaussian Processes for ordinal regression

问题或错误高度复杂的基础设施项目很少按计划运行，这是由于规划过程中的人为优化偏差、项目活动之间复杂的相互作用以及未能从过去的项目中系统地学习。nPlan积累了世界上最大的专有时间表数据集，并开发了机器学习建模技术，可以根据历史数据进行训练，以预测未来的风险。然而，在真实的世界中，训练模型的效用取决于评估用于预测未来结果的校准良好的概率分布。这个项目的目的是开发新的概率模型，优化这些概率分布的计算，通过描绘aleatoric和认识的贡献，使用贝叶斯推理的不确定性。开发图形和/或语言模型，以有效的数字表示时间表功能。2.识别具有相似图形和/或语言特征的代表性数据源，以评估概率模型。开发概率模型来推断进度特征和结果之间的关系。优化建模算法，使其可扩展到商业应用。博士项目描述概率模型的开发建立在nPlan已经开发的现有最先进的深度学习技术基础上。由于nPlan项目依赖于基于训练模型的模拟进度结果，因此预测的概率分布提供不确定性的真实表示非常重要。由于传统的深度学习模型没有明确地描述概率，因此该项目的目标是开发新的模型。将允许访问nPlan专有数据集和nPlan核心代码库，以实现高效数据阅读。博士项目的范围非常广泛，鼓励探索不同的项目数据类型和模型，同时提供nPlan和Girolami教授团队的专业指导。MRes知识MRes组件包括上面列出的目标1和2。由于nPlan数据集非常大，因此将在第一年内开发出在合适子集上训练的初步原型模型。这项工作包括识别具有相似结构的数据源（表示为有向非循环图的甘特图），用于概率模型的初始开发和评估。

Problem or ChallengeHighly complex infrastructure projects rarely run to schedule as a result of human optimisation bias during planning, complex interactions between project activities, and failure to learn systematically from past projects. nPlan has amassed the largest proprietary collection of schedules data in the World and developed machine learning modelling technologies that train on historical data to predict future risk. In the real world however, the utility of trained models depends on evaluating well-calibrated probability distributions for predicting future outcomes. The aim of this project is to develop novel probabilistic models that optimise computation of these probability distributions by delineating aleatoric and epistemic contributions to uncertainty using Bayesian inference.MRes/PhD Project Objectives1. Develop graph and/or language models for efficient numerical representation of schedule features.2. Identify representative data sources with similar graph and/or language features to evaluate probabilistic models.3. Develop probabilistic models that infer relationships between schedule features and outcomes.4. Optimise modelling algorithms to be scalable for commercial implementation.PhD Project DescriptionThe development of probabilistic models build upon existing state-of-the-art deep learning technologies already developed at nPlan Since the nPlan project depends on simulating outcomes of schedules based on trained models, it is important that predicted probability distributions provide realistic representations of uncertainty. Since conventional deep learning models do not explicitly characterise probabilities, the aim of the project is to develop novel models that do. Access will be given to the nPlan proprietary data set and the nPlan Core code-base for efficient data reading. The scope of the PhD project is purposefully broad to encourage exploration of different project data types and models, while providing guidance from the expertise within nPlan and the Prof. Girolami group.MRes ComponentThe MRes component includes objectives 1&2 listed above. Since the nPlan dataset is very large, preliminary prototype models trained on suitable subsets will be developed within the first year. This work includes identification of data sources with similar structures (Gantt charts represented as directed acyclic graphs) for initial development and evaluation of probabilistic models.