A Characterization of Probabilistic Inference
نویسنده
چکیده
Inductive Inference Machines (IIMs) attempt to identify functions given only input-output pairs of the functions. Pro6a6ilistic IIMs are defined, as is the probability that a probabilistic IIM identifies a function with respect to two common identification criteria: EX and BC. Let ID denote either of these criteria. Then IDpm&) is the family of sets of functions U for which there is a probabilistic IIM identifying every f E U with probability 2 p. It is shown that for all positive integers n, IDpmb(l/n) is properly contained in ]IDpmb( l/(n+l)), and that this discrete hierarchy is the “finest” possible. This hierarchy is related to others in the literature.
منابع مشابه
A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin
Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover ...
متن کاملA Probabilistic Characterization of Fuzzy Set Membership, with Application to Mixed Fuzzy-Probabilistic Inference
A simple probabilistic grounding of the ”fuzzy set membership degree” is presented, and used to provide definitions of the absolute and conditional probabilities of fuzzy sets. Among other possible applications, this allows fuzzy membership values to be coherently incorporated into probabilistic reasoning processes.
متن کاملAn Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملRule-based joint fuzzy and probabilistic networks
One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...
متن کاملLoad-Frequency Control: a GA based Bayesian Networks Multi-agent System
Bayesian Networks (BN) provides a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of load-frequency control (LFC). In practice, LFC systems use proportional-integral controllers. However since these controllers are designed using a linear model, the nonlinearities...
متن کاملAbductive Inference with Probabilistic Graphical Models
Starting from a general characterization of logical inferences, I consider abductive reasoning, which aims at finding likely causes for observed symptoms. Such inferences are not truth preserving and thus it is necessary to assess their conclusions, to compare different explanations of the same findings, and finally to select the “best” hypothesis. Since in a large number of applications probab...
متن کامل