Probabilistic Reasoning in Intelligent Systems

Target entity: Probabilistic Reasoning in Intelligent Systems
Context triple: [Judea Pearl, notableWork, Probabilistic Reasoning in Intelligent Systems]

• A. Probabilistic Graphical Models: Principles and Techniques
  Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
• B. Logical Foundations of Probability
  Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
• C. Bayesian networks
  Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
• D. Of Knowledge and Probability
  "Of Knowledge and Probability" is a section in John Locke’s *An Essay Concerning Human Understanding* that analyzes the nature, degrees, and limits of human knowledge in contrast with mere probability or belief.
• E. probabilistic graphical models
  Probabilistic graphical models are a framework in machine learning and statistics that represent complex joint probability distributions using graphs to capture conditional dependencies among random variables.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Probabilistic Reasoning in Intelligent Systems
Target entity description: Probabilistic Reasoning in Intelligent Systems is a foundational book by Judea Pearl that introduced Bayesian networks and revolutionized the use of probability theory for reasoning and decision-making in artificial intelligence.

• A. Probabilistic Graphical Models: Principles and Techniques
  Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
• B. Logical Foundations of Probability
  Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
• C. Bayesian networks
  Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
• D. Of Knowledge and Probability
  "Of Knowledge and Probability" is a section in John Locke’s *An Essay Concerning Human Understanding* that analyzes the nature, degrees, and limits of human knowledge in contrast with mere probability or belief.
• E. probabilistic graphical models
  Probabilistic graphical models are a framework in machine learning and statistics that represent complex joint probability distributions using graphs to capture conditional dependencies among random variables.
• F. None of above. chosen