• highlight the versatility and modeling power of Bayesian networks, and also discuss their limitations and failures, in order to help potential users to assess the adequacy of Bayesian networks to their needs;
• provide practical guidance on constructing and using Bayesian networks.

p.1 Real-world problems... are often described as complex... Furthermore... a variety of factors... tend to distort our judgment of a situation.

One way of trying to better handle reality - in spite of these limitations and biases - is to use representations of reality called models.

p.2 the purpose of a model is to satisfy the need of some person or organization having a particular interest in one or several aspects of the object, but not in a comprehensive understanding of its properties.

p.3 Definition 2 (Model) A model is a representation of an object, expressed in a specific language and in a usable form, and is intended to satisfy one or several need(s) of some stakeholder(s) of the object.

p.3 Models are thus used to produce information (evaluations, appropriate decisions or actions) on the basis of some input information, considered as valid. This process is called inference.

p.4 the way a model is constructed obviously depends on several factors, such as the nature of the object, the stakeholder's need(s), the available knowledge and information, the time and resources devoted to the model elaboration, etc. Nevertheless, we may identify two invariants in the process of constructing a model... Splitting the object into elements... Saying how it works: the modeling language

p.5 most successful or unsuccessful attempts of mankind to overcome the complexity of reality have involved, at some stage, a form [of] a graphical representation.

p.5 During the modeling process, the exact circumstances in which the model is going to be used (especially, what input data the model will process) are, to a large extent, unknown. Also, some of the attributes remain unknown when the model is used: the attributes which are at some stage unknown are more conveniently described by variables.

p.7 Doubt is a typically human faculty which can be considered as the basis of any scientific process... The construction of a probabilistic model requires the systematic examination of all possible values of each variable... it is hard to imagine a more precise representation of an object: each of the theoretically possible configurations of the object is considered, and to each of them is associated one element of the infinite set [0;1]. [JLJ - a probability between 0 and 1]

p.9 Following Descartes's precept of dividing the difficulties, one may try to split the set of n variables into several subsets of smaller sizes which can relatively be analyzed separately... Then the modeling problem can be transformed into two simpler ones.

p.11 In the lorry [truck] driver and doped athlete examples, we have identified the most direct and significant influences between the variables, and simplified the derivation of the joint probability distribution. By representing these influences in a graphical form, we now introduce the notion of [a] Bayesian network.

p.26 Inference The most crucial task of an expert system is to draw conclusions based on new evidence. The mechanism of drawing conclusions in a system that is based on a probabilistic graphical model is known as propagation of evidence. Propagation of evidence involves essentially updating probabilities given observed variables of a model (also known as belief updating).

p.31 Rule-based systems capture heuristic knowledge from the experts and allow for a direct construction of a classification relation... Rule-based systems may be expected to perform well for problems that cannot be modeled using causality as a guiding principle, or when a problem is too complicated to be modeled as a causal graph.

p.32 Bayesian networks are recognized as a convenient tool for modeling processes of medical reasoning. There are several features of Bayesian networks that are specially useful in modeling in medicine. One of these features is that they allow us to combine expert knowledge with existing clinical data.

p.54 The use of Bayesian networks in biomedical sciences can be traced as far back as the early decades of the 20th century, when Sewell Wright developed path analysis to aid the study of genetic inheritance. Neglected for many years, Bayesian Networks were reintroduced in the early 1980s as an analytic tool capable [of] encoding the information acquired from human experts. Compared to decision-rule based "expert-systems" that were limited in their ability to reason under uncertainty, Bayesian networks were probabilistic expert systems that used probability theory to account for uncertainty in automated reasoning for diagnostic and prognostic tasks. This type of probabilistic reasoning was made possible by the development of algorithms to propagate probabilistic information through a network.

p.71 Bayesian networks provide a flexible modeling framework to describe complex systems in a modular way.

p.84 The BN [Bayesian network] can provide useful information for crime risk factor analysis.

p.185 Bayesian networks provide a general and effective framework for knowledge representation and reasoning under uncertainty.

p.210 Once the BN [Bayesian network] has been constructed, it is enlarged by including decision and utility nodes, thus transforming it into an influence diagram.

p.384 Although Bayesian networks are certainly not the Holy Grail of artificial intelligence, they definitely are a solid basis for knowledge engineering. They allow us to use various sources of knowledge, even contradicting ones, to make knowledge embedded in data explicit, to use this knowledge for various types of problem solving, and finally to improve it through online learning.

Artificial intelligence remains a challenge for the next decades. Indeed, intelligence cannot be limited to inference and learning, but requires action. Embedding artificial intelligence systems in the real world is probably the next challenge of artificial intelligence, far beyond simply connecting an offline "artificially intelligent system" to external sensors and actuators.