Towards an Unsupervised Bayesian Network Pipeline for Explainable Prediction, Decision Making and Discovery

An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth

Constitution and Causal Roles

Alexander Gebharter has recently proposed to use Bayesian network causal discovery methods to identify the constitutive dependencies that underwrite mechanistic explanations. The proposal depends on using the assumptions of the causal Bayesian network framework to implicitly define mechanistic constitution as a kind of deterministic direct causal dependence. The aim of this paper is twofold. In the first half, we argue that Gebharter’s proposal incurs severe conceptual problems. In the second half, we present an alternative way to bring Bayesian network tools to bear on the issue of understanding mechanistic constitution. More precisely, our proposal interprets constitution as the relation explaining why a target phenomenon has its characteristic causal role in terms of the causal roles of some of its spatiotemporal parts---where the notion of causal role is probabilistically understood

Non-stratified chain event graphs : dynamic variants, inference and applications

A chain event graph (CEG) is a graphical model that is constructed by identifying the probabilistic symmetries within the tree-based description of a process. CEGs generalise Bayesian networks (BNs) by representing context-specific conditional independencies within their graph topologies. The CEG literature, through the stratified CEG class, has demonstrated efficacy over BNs in modelling processes with contextual independence structures. CEGs are also suited to modelling ‘asymmetric’ processes with event spaces that do not admit a product space structure. While such processes are common in many domains, they are not easily and effectively modelled by BNs and other graphical models with variable-based topologies. This thesis presents the first exposition of the theory and applications of the more general non-stratified CEG class that models asymmetric processes. We demonstrate, through modelling of an asymmetric public health intervention, that the CEG provides a superior representation than the BN in non-product space settings. We then present a novel dynamic variant of CEGs called the continuous time dynamic CEG which has an approximate semi-Markov process representation. We show that this dynamic class generalises and vastly expands the existing subclass of extended dynamic CEGs, first studied in Barclay et al. (2015). We develop semantics unique to this class and propose a dynamic inference scheme for it together with a novel continuous time probability propagation algorithm. In doing this, we are able to utilise any observed information about the temporal evolution of the process to update our beliefs. Finally, we demonstrate by modelling the evolution of criminal collaborations how the Bayesian paradigm allows us to combine a dynamic CEG model with other disparate models – after due consideration of the independencies between these models – where each model is a component describing a distinct aspect of a complex longitudinal process

Analyzing Structured Scenarios by Tracking People and Their Limbs

The analysis of human activities is a fundamental problem in computer vision. Though complex, interactions between people and their environment often exhibit a spatio-temporal structure that can be exploited during analysis. This structure can be leveraged to mitigate the effects of missing or noisy visual observations caused, for example, by sensor noise, inaccurate models, or occlusion. Trajectories of people and their hands and feet, often sufficient for recognition of human activities, lead to a natural qualitative spatio-temporal description of these interactions. This work introduces the following contributions to the task of human activity understanding: 1) a framework that efficiently detects and tracks multiple interacting people and their limbs, 2) an event recognition approach that integrates both logical and probabilistic reasoning in analyzing the spatio-temporal structure of multi-agent scenarios, and 3) an effective computational model of the visibility constraints imposed on humans as they navigate through their environment. The tracking framework mixes probabilistic models with deterministic constraints and uses AND/OR search and lazy evaluation to efficiently obtain the globally optimal solution in each frame. Our high-level reasoning framework efficiently and robustly interprets noisy visual observations to deduce the events comprising structured scenarios. This is accomplished by combining First-Order Logic, Allen's Interval Logic, and Markov Logic Networks with an event hypothesis generation process that reduces the size of the ground Markov network. When applied to outdoor one-on-one basketball videos, our framework tracks the players and, guided by the game rules, analyzes their interactions with each other and the ball, annotating the videos with the relevant basketball events that occurred. Finally, motivated by studies of spatial behavior, we use a set of features from visibility analysis to represent spatial context in the interpretation of human spatial activities. We demonstrate the effectiveness of our representation on trajectories generated by humans in a virtual environment