46,827 research outputs found
Ancient Indian Logic and Analogy
B.K.Matilal, and earlier J.F.Staal, have suggested a reading of the `Nyaya five limb schema' (also sometimes referred to as the Indian Schema or Hindu Syllogism) from Gotama's Nyaya-Sutra in terms of a binary occurrence relation. In this paper we provide a rational justification of a version of this reading as Analogical Reasoning within the framework of Polyadic Pure Inductive Logic
Induction without Probabilities
A simple indeterministic system is displayed and it is urged that we cannot responsibly infer inductively over it if we presume that the probability calculus is the appropriate logic of induction. The example illustrates the general thesis of a material theory of induction, that the logic appropriate to a particular domain is determined by the facts that prevail there
The Counterpart Principle of Analogical Support by Structural Similarity
We propose and investigate an Analogy Principle in the context of Unary Inductive Logic based on a notion of support by structural similarity which is often employed to motivate scientific conjectures
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
Semantics for Probabilistic Inference
A number of writers(Joseph Halpern and Fahiem Bacchus among them) have
offered semantics for formal languages in which inferences concerning
probabilities can be made. Our concern is different. This paper provides a
formalization of nonmonotonic inferences in which the conclusion is supported
only to a certain degree. Such inferences are clearly 'invalid' since they must
allow the falsity of a conclusion even when the premises are true.
Nevertheless, such inferences can be characterized both syntactically and
semantically. The 'premises' of probabilistic arguments are sets of statements
(as in a database or knowledge base), the conclusions categorical statements in
the language. We provide standards for both this form of inference, for which
high probability is required, and for an inference in which the conclusion is
qualified by an intermediate interval of support.Comment: Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992
Probabilistic call by push value
We introduce a probabilistic extension of Levy's Call-By-Push-Value. This
extension consists simply in adding a " flipping coin " boolean closed atomic
expression. This language can be understood as a major generalization of
Scott's PCF encompassing both call-by-name and call-by-value and featuring
recursive (possibly lazy) data types. We interpret the language in the
previously introduced denotational model of probabilistic coherence spaces, a
categorical model of full classical Linear Logic, interpreting data types as
coalgebras for the resource comonad. We prove adequacy and full abstraction,
generalizing earlier results to a much more realistic and powerful programming
language
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