Results 11  20
of
7,486
Probability Concepts For An Expert System Used For Data Fusion
"... Probability concepts for rulebaaed expert systems are developed that are compatible with probability used in data fusion of imprecise information Procedures for treating probabilistic evidence are presented, which include the effects of statistical dependence. Confidence limits are defined as bein ..."
Abstract
 Add to MetaCart
Probability concepts for rulebaaed expert systems are developed that are compatible with probability used in data fusion of imprecise information Procedures for treating probabilistic evidence are presented, which include the effects of statistical dependence. Confidence limits are defined
WinBUGS  a Bayesian modelling framework: concepts, structure, and extensibility
 STATISTICS AND COMPUTING
, 2000
"... WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an ob ..."
Abstract

Cited by 430 (6 self)
 Add to MetaCart
WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
Abstract

Cited by 440 (17 self)
 Add to MetaCart
In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability
ASSESSING AND EDUCATING PRESCHOOL TEACHERS ON PROBABILITY CONCEPTS IN THE CLASSROOM
"... This study refers to an experiment on teaching probabilities, conducted in Greece at preschools in Athens and Ioannina. The aim of this study was to assess teachers on how they introduced common statistical concepts to children throughout the academic year of 20042005. Moreover, this study presents ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This study refers to an experiment on teaching probabilities, conducted in Greece at preschools in Athens and Ioannina. The aim of this study was to assess teachers on how they introduced common statistical concepts to children throughout the academic year of 20042005. Moreover, this study
UNDERSTANDING OF CHANCE AND PROBABILITY CONCEPTS AMONG FIRST YEAR UNIVERSITY STUDENTS
"... In higher education, the quality of learning outcomes and the way in which students approach their studies have been shown to be related to the students ’ conceptions of a subject and how it is learned. In a number of South African universities, an area of concern is the failure rate at the end of t ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
to their conceptualisation of the subject and their learning experiences. This study is an attempt to gain insight into first year students ’ conceptions of chance and probability concepts and their approaches to learning statistics.
Bayesian Description Logics. In:
 Proc. of DL’14. CEUR Workshop Proceedings,
, 2014
"... Abstract This chapter considers, on the one hand, extensions of Description Logics by features not available in the basic framework, but considered important for using Description Logics as a modeling language. In particular, it addresses the extensions concerning: concrete domain constraints; moda ..."
Abstract

Cited by 394 (49 self)
 Add to MetaCart
; modal, epistemic, and temporal operators; probabilities and fuzzy logic; and defaults. On the other hand, it considers nonstandard inference problems for Description Logics, i.e., inference problems thatunlike subsumption or instance checkingare not available in all systems, but have turned out
A Theory of Program Size Formally Identical to Information Theory
, 1975
"... A new definition of programsize complexity is made. H(A;B=C;D) is defined to be the size in bits of the shortest selfdelimiting program for calculating strings A and B if one is given a minimalsize selfdelimiting program for calculating strings C and D. This differs from previous definitions: (1) ..."
Abstract

Cited by 380 (15 self)
 Add to MetaCart
concept of information theory. For example, H(A;B) = H(A) + H(B=A) + O(1). Also, if a program of length k is assigned measure 2 \Gammak , then H(A) = \Gamma log 2 (the probability that the standard universal computer will calculate A) +O(1). Key Words and Phrases: computational complexity, entropy
REASONING DEVELOPMENT OF A HIGH SCHOOL STUDENT ABOUT PROBABILITY CONCEPT
"... In this article the development of reasoning of a highschool student on the concept of probability is described from the inferences formulated as he solved three problems. An adaptation of Jones et al.'s (1999) framework was used to indicate the important characteristics in his reasoning. A ..."
Abstract
 Add to MetaCart
In this article the development of reasoning of a highschool student on the concept of probability is described from the inferences formulated as he solved three problems. An adaptation of Jones et al.'s (1999) framework was used to indicate the important characteristics in his reasoning
The class imbalance problem: A systematic study
 Intelligent Data Analysis
, 2002
"... Abstract In machine learning problems, dierences in prior class probabilitiesor class imbalanceshave been reported to hinder the performance of some standard classi ers, such as decision trees. This paper presents a systematic study aimed at answering three dierent questions. First, we attempt to ..."
Abstract

Cited by 310 (2 self)
 Add to MetaCart
Abstract In machine learning problems, dierences in prior class probabilitiesor class imbalanceshave been reported to hinder the performance of some standard classi ers, such as decision trees. This paper presents a systematic study aimed at answering three dierent questions. First, we attempt
Results 11  20
of
7,486