Zobrazeno 1 - 10
of 411
pro vyhledávání: '"Neville, K. A."'
Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been critical about the accuracy and usefulness of Large Language Models (LLM
Externí odkaz:
http://arxiv.org/abs/2407.18607
Sepsis is a life-threatening and serious global health issue. This study combines knowledge with available hospital data to investigate the potential causes of Sepsis that can be affected by policy decisions. We investigate the underlying causal stru
Externí odkaz:
http://arxiv.org/abs/2406.09207
Causal Bayesian Networks (CBNs) are an important tool for reasoning under uncertainty in complex real-world systems. Determining the graphical structure of a CBN remains a key challenge and is undertaken either by eliciting it from humans, using mach
Externí odkaz:
http://arxiv.org/abs/2310.11154
Autor:
Constantinou, Anthony, Kitson, Neville K., Liu, Yang, Chobtham, Kiattikun, Hashemzadeh, Arian, Nanavati, Praharsh A., Mbuvha, Rendani, Petrungaro, Bruno
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for
Externí odkaz:
http://arxiv.org/abs/2305.03859
Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are bec
Externí odkaz:
http://arxiv.org/abs/2206.08952
In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to establish edge orientations, which is why many BN structure learning
Externí odkaz:
http://arxiv.org/abs/2112.10574
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function. Some algorithms offer exact solutions that gua
Externí odkaz:
http://arxiv.org/abs/2112.00398
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-w
Externí odkaz:
http://arxiv.org/abs/2109.11415
Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions. The graphical st
Externí odkaz:
http://arxiv.org/abs/2102.00473
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across stu
Externí odkaz:
http://arxiv.org/abs/2005.09020