Abstract
The general problem of drawing inferences from uncertain or incomplete evidence has invited a variety of technical approaches, some mathematically rigorous and some largely informal and intuitive. Most current inference systems in artificial intelligence have emphasized intuitive methods, because the absence of adequate statistical samples forces a reliance on the subjective judgment of human experts. We describe in this paper a subjective Bayesian inference method that realizes some of the advantages of both formal and informal approaches. Of particular interest are the modifications needed to deal with the inconsistencies usually found in collections of subjective statements.
Keywords
Computer scienceInferenceArtificial intelligenceVariety (cybernetics)Machine learningBayesian probabilityBayesian inferenceStatistical inferenceFrequentist inferenceMathematicsStatistics
Affiliated Institutions
Related Publications
Wald Lecture: On the Bernstein-von Mises theorem with infinite-dimensional parameters
If there are many independent, identically distributed\nobservations governed by a smooth, finite-dimensional statistical model, the\nBayes estimate and the maximum likelihood e...
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...
Empirical bayes methods and false discovery rates for microarrays
Abstract In a classic two‐sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment...
Bootstrap Methods: Another Look at the Jackknife
We discuss the following problem: given a random sample $\\mathbf{X} = (X_1, X_2, \\cdots, X_n)$ from an unknown probability distribution $F$, estimate the sampling distribution...
Practical Bayesian Density Estimation Using Mixtures of Normals
Abstract Mixtures of normals provide a flexible model for estimating densities in a Bayesian framework. There are some difficulties with this model, however. First, standard ref...
Publication Info
- Year
- 1976
- Type
- article
- Pages
- 1075-1075
- Citations
- 549
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
549
OpenAlex
Cite This
Richard O. Duda,
Peter E. Hart,
Nils J. Nilsson
(1976).
Subjective bayesian methods for rule-based inference systems.
, 1075-1075.
https://doi.org/10.1145/1499799.1499948
Identifiers
- DOI
- 10.1145/1499799.1499948