By Annabelle McIver
Probabilistic concepts are more and more being hired in desktop courses and structures simply because they could elevate potency in sequential algorithms, let another way nonfunctional distribution purposes, and make allowance quantification of probability and safeguard ordinarily. This makes operational types of ways they paintings, and logics for reasoning approximately them, tremendous important.
Abstraction, Refinement and facts for Probabilistic Systems provides a rigorous method of modeling and reasoning approximately computers that comprise chance. Its foundations lie in conventional Boolean sequential-program logic—but its extension to numeric instead of simply true-or-false judgments takes it a lot additional, into parts akin to randomized algorithms, fault tolerance, and, in dispensed structures, almost-certain symmetry breaking. The presentation starts with the well-known "assertional" sort of software improvement and maintains with expanding specialization: half I treats probabilistic application common sense, together with many examples and case reports; half II units out the unique semantics; and half III applies the method of complicated fabric on temporal calculi and two-player games.
Topics and features:
* provides a basic semantics for either likelihood and demonic nondeterminism, together with abstraction and knowledge refinement
* Introduces readers to the most recent mathematical learn in rigorous formalization of randomized (probabilistic) algorithms * Illustrates by means of instance the stairs worthy for construction a conceptual version of probabilistic programming "paradigm"
* Considers result of a wide and built-in examine workout (10 years and carrying on with) within the modern sector of "quantitative" software logics
* contains precious chapter-ending summaries, a entire index, and an appendix that explores substitute approaches
This obtainable, centred monograph, written through overseas gurus on probabilistic programming, develops an important beginning subject for contemporary programming and platforms improvement. Researchers, desktop scientists, and complex undergraduates and graduates learning programming or probabilistic structures will locate the paintings an authoritative and crucial source text.
Read or Download Abstraction, Refinement and Proof for Probabilistic Systems PDF
Similar compilers books
For classes in Cobol Programming. This variation is designed to fulfill all your COBOL wishes - on a number of structures. The textbook covers all simple COBOL components, with extra chapters at the yr 2000 challenge, based programming and layout, debugging, subprograms, desk processing, sorting, reveal I/O, sequential dossier upkeep, listed records, and object-oriented COBOL.
Computerized Quantum laptop Programming presents an advent to quantum computing for non-physicists, in addition to an advent to genetic programming for non-computer-scientists. The booklet explores a number of ways that genetic programming can help computerized quantum machine programming and offers special descriptions of particular innovations, besides a number of examples in their human-competitive functionality on particular difficulties.
Dieses Buch vermittelt Techniken zur Formalisierung der Semantik (Bedeutungsinhalte) von Programmiersprachen. Zunächst werden unterschiedliche Formalisierungsansätze (die operationelle, denotationelle und axiomatische Semantik) vorgestellt und diskutiert. Anschließend wird die mathematische Theorie der semantischen Bereiche entwickelt, die bei der zur Zeit wichtigsten, der denotationellen Methode, Anwendung findet.
Construct your personal languages with ANTLR v4, utilizing ANTLR's new complicated parsing know-how. during this publication, you will learn the way ANTLR immediately builds a knowledge constitution representing the enter (parse tree) and generates code which can stroll the tree (visitor). you should use that mix to enforce information readers, language interpreters, and translators.
Extra info for Abstraction, Refinement and Proof for Probabilistic Systems
Above assumption about postE, postE as above, but for postE ✷ This kind of reasoning is nothing new for standard programs, and indeed is usually taken for granted (although its formal justiﬁcation appeals to conjunctivity). 8 Interaction of probabilisticand demonic choice We conclude with some illustrations of the interaction of demonic and probabilistic choice. Consider two variables x, y, one chosen demonically and the other probabilistically. Suppose ﬁrst that x is chosen demonically and y probabilistically, and take post-expectation [x = y].
1 Example: an inductive termination argument Proper post-expectations . . . . . . . . 1 The martingale revisited . . . . . . Bounded vs. unbounded expectations . . . . . 1 Unbounded invariants: a counter-example . Informal proof of the loop rule . . . . . . . . . . . . . . . . 38 2. 1 Introduction: loops via recursion We saw in Chap. 1 that iteration is a special case of recursion. postE cannot be given a purely syntactic deﬁnition for general recursive prog — the deﬁnition given earlier (Fig.
38 2. 1 Introduction: loops via recursion We saw in Chap. 1 that iteration is a special case of recursion. postE cannot be given a purely syntactic deﬁnition for general recursive prog — the deﬁnition given earlier (Fig. 3) is semantic, a least ﬁxed-point over expectation transformers. It does give us an algebraic property of recursive programs, viz. 6) below however for an example of its use in spite of that. 1) we have immediately the property that do pred → prog od = ( prog ; do pred → prog od ) if pred else skip .