Probabilistic reasoning in expert systems - theory and algorithms

  title={Probabilistic reasoning in expert systems - theory and algorithms},
  author={Richard E. Neapolitan},
This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks… Expand
On the combination of logical and probabilistic models for information analysis
This work defines a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks and shows that hard evidential updates force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. Expand
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The design of a belief network reformulation of the diagnostic rule-based expert system HEPAR is discussed, finding that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from HEPARI. Expand
Bayesian Networks for Logical Reasoning
By identifying and pursuing analogies between causal and logical influence I show how the Bayesian network formalism can be applied to reasoning about logical deductions. Despite the fact thatExpand
A pragmatic Bayesian platform for automating scientific induction
It is argued that Bayesian confirmation theory provides a general normative theory of inductive learning and therefore should have a role in any artificially intelligent system that is to learn inductively about its world. Expand
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It is shown that certainty-factor-like structures occur frequently in practical Bayesian network models as causal independence assumptions, and this insight may lead to a reappraisal of the certainty-Factor model. Expand
Certainty-Factor-Like Structures in Bayesian Networks
It is shown that certainty-factor-like structures occur frequently in practical Bayesian network models as causal independence assumptions, and this insight may lead to a reappraisal of the certainty-Factor model. Expand
Bayesian Network and Variable Elimination Algorithm for Reasoning under Uncertainty
A common task for a Bayesian network is to perform inference by computing to determine various probabilities of interest from the model. We are using an algorithm for construction of Bayesian networkExpand
Diagnostic reasoning based on a genetic algorithm operating in a Bayesian belief network
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The results of the present study indicate that in a given context of observed symptoms, a genetically generated population of possible solutions retains much of the diagnostic power contained in the full model: the disease probabilities as occuring in this population and as calculated from theFull model are strongly rank-correlated. Expand
A Bayesian learning approach to inconsistency identification in model-based systems engineering
An effective method for identifying inconsistencies throughout the life cycle that is capable of drawing conclusions from an incomplete, but continuously refined description of a system should be based on Bayesian updating. Expand
Bericht Nr . 126 Logic Is Not Enough : Why Reasoning About Another Person ’ s Beliefs Is Reasoning Under Uncertainty
A system that reasons about the beliefs of a person must in general be able to ascribe a good deal of general background knowledge to that person, often in the absence of reliable evidence that theExpand


Probabilistic reasoning in intelligent systems - networks of plausible inference
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The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. Expand
Probabilistic Logic
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The method described in the present paper combines logic with probability theory in such a way that probabilistic logical entaihnent reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1. Expand
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