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The relative likelihood of a particular outcome among all possible outcomes.


the likelihood of a particular uncertain event occurring, measured on a scale from 0.0 (such an event is impossible) to 1.0 (such an event is certain to occur). People generally estimate probabilities on the basis of the relative frequency with which an event has occurred in the past, under given circumstances, and generalize from this past experience. In some circumstances it is easy to estimate the proportion of occasions on which an event occurs; for example, the probability of getting ‘heads’ when flipping a balanced coin is 0.5 because with such a coin in the long run we would get 50% ‘heads’ and 50% ‘tails’. In estimating probabilities in business situations, though, there may be no or only a few previous experiences that can be used to gauge the relative frequency of an event occurring. See also RISK AND UNCERTAINTY.
References in periodicals archive ?
Strategies to effectively reduce probabilistic reasoning errors remain unclear, but heuristics and cognitive biases are inevitable in the face of time-pressured medical decision-making in clinical reality.
The role of probabilistic reasoning has not yet been recognized in the sentencing context.
Kahneman also questions experts' ability to avoid fallacies in probabilistic reasoning.
By studying babies and young children, scientists can test whether probabilistic reasoning is present before life experiences begin sculpting the mind.
The response, previously seen as incorrect, displays correct probabilistic reasoning when declaring that a mixture of heads and tails is more likely--three times--than no mixture.
Topics include but are not limited to the following: agent-based and multi-agent systems, cognitive modeling and human interaction, commonsense reasoning, computer vision, constraint satisfaction, search, and optimization, evolutionary computation, game playing and interactive entertainment, information retrieval, integration, and extraction, knowledge acquisition and ontologies, knowledge representation and reasoning, machine learning and data mining, model-based systems, multidisciplinary ai, natural language processing, planning and scheduling, probabilistic reasoning, robotics, web and information systems.
He has authored three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000).
VoxGen said that it intends to develop a statistically rigorous probabilistic reasoning system to combine these two incongruent data points.
The chapters of this volume offer a wide range of areas where the technology of soft computing, fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning are applied, including medicine, bioinformatics, natural resource management, and industry.
Girotto and Gonzalez (Chapter 8) extend MMT to explain naive probabilistic reasoning.
But, as Labov (1994:450) points out, "[t]he Neogrammarian viewpoint must of course be modified to accept stochastic regularities in place of absolute rules", and he speculates that "[the Neogrammarians] would not have been as likely to welcome the tools of statistical analysis and probabilistic reasoning, since they were committed to discrete solutions" (1994:470).

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