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Author: Bruno De Finetti
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 300
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Book Description
Author: Bruno De Finetti
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 300
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Book Description
Author: Gilbert Harman
Publisher: MIT Press
ISBN: 0262263157
Category : Psychology
Languages : en
Pages : 119
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Book Description
The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
Author:
Publisher: Elsevier
ISBN: 0080930964
Category : Philosophy
Languages : en
Pages : 1253
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Book Description
Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling "restricted by their disciplines or thinking "piecemeal in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers' favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines. - Provides a bridge between philosophy and current scientific findings - Covers theory and applications - Encourages multi-disciplinary dialogue
Author: Corrado Gini
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 496
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Book Description
Author: Ian Hacking
Publisher: Cambridge University Press
ISBN: 9780521775014
Category : Mathematics
Languages : en
Pages : 326
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Book Description
An introductory 2001 textbook on probability and induction written by a foremost philosopher of science.
Author: Ian Hacking
Publisher: Cambridge University Press
ISBN: 9780521318037
Category : Mathematics
Languages : en
Pages : 226
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Book Description
Includes an introduction, contextualizing his book in light of developing philosophical trends.
Author: David L. Dowe
Publisher: World Scientific
ISBN: 9814530638
Category : Artificial intelligence
Languages : en
Pages : 423
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Book Description
Author: Hantao Zhang
Publisher: Springer Science & Business Media
ISBN: 9400916752
Category : Computers
Languages : en
Pages : 223
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Book Description
It has been shown how the common structure that defines a family of proofs can be expressed as a proof plan [5]. This common structure can be exploited in the search for particular proofs. A proof plan has two complementary components: a proof method and a proof tactic. By prescribing the structure of a proof at the level of primitive inferences, a tactic [11] provides the guarantee part of the proof. In contrast, a method provides a more declarative explanation of the proof by means of preconditions. Each method has associated effects. The execution of the effects simulates the application of the corresponding tactic. Theorem proving in the proof planning framework is a two-phase process: 1. Tactic construction is by a process of method composition: Given a goal, an applicable method is selected. The applicability of a method is determined by evaluating the method's preconditions. The method effects are then used to calculate subgoals. This process is applied recursively until no more subgoals remain. Because of the one-to-one correspondence between methods and tactics, the output from this process is a composite tactic tailored to the given goal. 2. Tactic execution generates a proof in the object-level logic. Note that no search is involved in the execution of the tactic. All the search is taken care of during the planning process. The real benefits of having separate planning and execution phases become appar ent when a proof attempt fails.
Author: Joseph B. Kadane
Publisher: Cambridge University Press
ISBN: 9780521649759
Category : Mathematics
Languages : en
Pages : 402
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Book Description
This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. There are four principal themes to the collection: cooperative, non-sequential decisions; the representation and measurement of 'partially ordered' preferences; non-cooperative, sequential decisions; and pooling rules and Bayesian dynamics for sets of probabilities. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists.
Author: C.S. Wallace
Publisher: Springer Science & Business Media
ISBN: 0387276564
Category : Mathematics
Languages : en
Pages : 436
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Book Description
Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks’ arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton’s insight, we may never have made the connection between inference and brief encoding, which is the heart of this work.
