/ '" '" / Page 1 JUDEA PEARL GRAPHICAL MODELS FOR PROBABILISTIC AND CAUSAL
REASONING 1 INTRODUCTION This chapter surveys the development of graphical models …

Conventional maximum power point tracking (MPPT) algorithms fails to track peak power
from a solar photovoltaic panel (SPV) effectively under rapidly changing atmospheric and …

Finding subsets of a dataset that somehow deviate from the norm, ie where something
interesting is going on, is a classical Data Mining task. In traditional local pattern mining …

Abstract The Bayesian Network is a kind of probabilistic graphical models, having been
applied to various fields for inference and learning. A quantum-like Bayesian Network has …

Bayesian networks (BNs) have gained increasing attention in recent years. One key issue in
Bayesian networks is parameter learning. When training data is incomplete or sparse or …

Bayesian network (BN) is one of the most classical probabilistic graphical models. It has
been widely used in many areas, such as artificial intelligence, pattern recognition, and …

Bayesian networks (BNs) constitute a powerful framework for probabilistic reasoning and
have been extensively used in different research domains. This paper presents an improved …

This paper proposes a systematic framework using Bayesian networks to aggregate the
uncertainty from multiple sources for the purpose of uncertainty quantification (UQ) in the …

Forensic medical practitioners and scientists have for several years sought improved
decision support for determining and managing care and release of prisoners with mental …

Jeremy W Coid, 1* Simone Ullrich, 1 Constantinos Kallis, 1 Mark Freestone, 1 Rafael
Gonzalez, 1 Laura Bui, 1 Artemis Igoumenou, 1 Anthony Constantinou, 2 Norman Fenton, 2 …