Data Science: Bayesian Network (Definitions)

"A mathematic model in graphic form that represents a set of variables and their probabilistic independencies. It can be used, for example, to calculate the probability of a patient having a specific disease." (Attila Benko & Cecília S Lányi, "History of Artificial Intelligence", 2009) 

"A Bayesian network is a set of causally interrelated variables represented graphically in which the input information is generally subjective and can be updated in light of empirical data, by using Bayes’ theorem." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

"A type of neural network. The Bayesian network is based on the fundamentals of probability theory." (Meta S Brown, "Data Mining For Dummies", 2014)

"A Bayesian network is a directed acyclical graph (there are no cycles in the graph) that is composed of three basic elements: 
nodes: each feature in a domain is represented by a single node in the graph.
edges: nodes are connected by directed links; the connectivity of the links in a graph encodes the influence and conditional independence relationships between nodes. 
conditional probability tables: each node has a conditional probability table (CPT) associated with it. A CPT lists the probability distribution of the feature represented by the node conditioned on the features represented by the other nodes to which a node is connected by edges." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, worked examples, and case studies", 2015) 

"A representation of knowledge in the form of a directed acyclic graph representing random variables as nodes and their conditional dependencies as edges." (Petr Berka, "Machine Learning", 2015)

"They are acyclic graphical models that capture conditional dependence among random variables. Each node is associated with a function that gives the probability of finding the variable in a given state, given particular states of its parent variables." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"A graph model representing random variables with their conditional dependencies." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)

"A particular type of statistical model that represents a set of variables and their conditional dependencies. It is usually used to make previsions in a great variety of events." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"A model that represents and calculates the probabilistic relationships between a set of random variables and an uncertain domain via a directed acyclic graph." (Accenture)

"Bayesian Neural Networks (BNNs) refers to extending standard networks with posterior inference in order to control over-fitting. From a broader perspective, the Bayesian approach uses the statistical methodology so that everything has a probability distribution attached to it, including model parameters (weights and biases in neural networks). In programming languages, variables that can take a specific value will turn the same result every-time you access that specific variable." (Databricks) [source]