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Integrating Logical Reasoning and Probabilistic Chain Graphs
Integrating Logical Reasoning and Probabilistic Chain Graphs

... do not occur as head of a clause are called assumables. From a logical point of view, the ‘:’ operator has the meaning of a conjunction; it is only included in the syntax to allow separating atoms that are templates from non-template atoms. The basic idea is to use atoms D and Bi to introduce specifi ...
Estimating Network Layer Subnet Characteristics via Statistical Sampling
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Justification logic with approximate conditional probabilities
Justification logic with approximate conditional probabilities

... applications and has been successfully employed to analyze many different epistemic situations [2, 3, 6, 10, 12]. Also dynamic epistemic logics and certain forms of defeasible knowledge have been studied in justification logics [7, 8, 9, 11, 23, 33]. In a general setting, justifications need not to ...
Saturation of Sets of General Clauses
Saturation of Sets of General Clauses

... W.l.o.g. we may assume that clauses in N are pairwise variabledisjoint. (Otherwise make them disjoint, and this renaming process changes neither Res(N) nor GΣ (N).) Let C ′ ∈ Res(GΣ (N)), meaning (i) there exist resolvable ground instances Dσ and C ρ of N with resolvent C ′ , or else (ii) C ′ is a f ...
Predicate Logic - Teaching-WIKI
Predicate Logic - Teaching-WIKI

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Introduction to Formal Logic - Web.UVic.ca

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Justifying Underlying Desires for Argument
Justifying Underlying Desires for Argument

... from given knowledge and desires. They, however, do not address the situations in which there are no means for realizing the given desires nor desires derived from the sum of the desires and knowledge using these reasoning. In [11], the authors give defeasible inference rules transferring a modal op ...
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Introduction to Statistics for Researchers - Oak
Introduction to Statistics for Researchers - Oak

< 1 2 3 4 5 6 7 ... 43 >

Statistical inference

Statistical inference is the process of deducing properties of an underlying distribution by analysis of data. Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be sampled from a larger population.Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and does not assume that the data came from a larger population.
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