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2 edition of SIGPI, a user"s manual for fast computation of the probabilistic performance of complex systems found in the catalog.

SIGPI, a user"s manual for fast computation of the probabilistic performance of complex systems

C. J. Patenaude

SIGPI, a user"s manual for fast computation of the probabilistic performance of complex systems

by C. J. Patenaude

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  • 12 Currently reading

Published by Division of Reactor System Safety, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission in Washington, DC .
Written in English

    Subjects:
  • Risk assessment -- Databases -- Handbooks, manuals, etc.,
  • Probabilities -- Databases -- Handbooks, manuals, etc.

  • Edition Notes

    Statementprepared by C.J. Patenaude.
    ContributionsU.S. Nuclear Regulatory Commission. Division of Reactor System Safety., Lawrence Livermore National Laboratory.
    The Physical Object
    Paginationvi, 65 p. :
    Number of Pages65
    ID Numbers
    Open LibraryOL15352085M

    The scope of this research is to develop new methods and tools for high performance and computationally efficient control of uncertain complex systems. We take a probabilistic approach, considering that the uncertainty affecting the system has a probabilistic nature. In contrast to common stochastic control methods, a key feature of the developed techniques is that they . Optimizing Quality for Probabilistic Skyline Computation and Probabilistic Similarity Search Abstract: Probabilistic queries have been extensively explored to provide answers with confidence, in order to support the real-life applications struggling with uncertain data, such as sensor networks and data by: 1.

    users choose more (or less) secure passwords. To do this, one obtains password datasets chosen by users under difference circumstances, and then uses a password model to comparethe relative strengths of these sets of -2 research aims at finding the best password models. Such a model can.   Probabilistic inference is a widely-used, rigorous approach for processing ambiguous information based on models that are uncertain or incomplete. However, models and inference algorithms can be.

    From Algorithms to Z-Scores book. Read reviews from world’s largest community for readers. This is a textbook for a course in mathematical probability an /5. / Probabilistic Systems Analysis (Fall ) Problem Set 2: Solutions Due Septem 1. (a) The tree representation during the winter can be drawn as the following: Rain The forecast is "Rain" p. No Rain Rain 1-p The forecast is "No Rain" No RainFile Size: KB.


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SIGPI, a user"s manual for fast computation of the probabilistic performance of complex systems by C. J. Patenaude Download PDF EPUB FB2

Get this from a library. SIGPI, a user's manual for fast computation of the probabilistic performance of complex systems.

[C J Patenaude; U.S. Nuclear Regulatory Commission. Division of Reactor System Safety.; Lawrence Livermore National Laboratory.]. Description. A probabilistic Turing machine is a type of nondeterministic Turing machine in which each nondeterministic step is a "coin-flip"—that is, there are two possible next moves and the computation probabilistically selects which move to take.

Formal definition. A probabilistic Turing machine can be formally defined as the 7-tuple = (,,), where. A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

Most tasks require a person or an automated system to reason―to reach conclusions based on available by: described in the tutorial chapter of this manual. The experiments section at the end of this manual reports on some perfor-mance experiments with MayBMS.

Unfortunately, at the time of writing this, no benchmark for probabilistic database systems exists, so these experiments are necessarily somewhat ad-hoc. AcknowledgmentsCited by: 1. Differentiate between Deterministic and Probabilistic Systems by Dinesh Thakur Category: Information and System Concepts If the description of the system state at a particular point of time of its operation is given, the next state can be perfectly predicted.

This popular book makes a noble attempt at unifying the many different types of probabilistic models used in artificial intelligence. It seems like a good reference manual for people who are already familiar with the fundamental concepts of commonly used probabilistic graphical models/5.

Availability evaluation, Bayesian networks, Complex systems, Availability reduction factor. INTRODUCTION. In the most current papers, the evaluation of dependability methods (evaluation of reliability and availability) are generally reserved for the simple systems (series and parallel systems) or for the components.

Index terms—probabilistic model, analytical model, RFID. INTRODUCTION In this paper a comparison between a fast probabilistic model and an analytical Full-Wave model is presented.

Both models aim to evaluate the identification performance of passive RFID systems operating at the UHF frequency band (MHzMHz).

for quantum computation will follow. 1 Model for Probabilistic Computation Overview Probabilistic computers can use randomness to determine which operations to perform on their inputs. Thus, the state at any given moment and the final output of a computation are both random variables.

Modern probabilistic models regularly involve thousands or millions of stochastic choices with complex dependencies 22 Engineering languages, equipped with powerful primitives and closed under ex-pressive means of composition and abstraction, form the basis of our ability to synthesize and analyze complex systems.

computational performance of our proposed power system probabilistic and security analysis applications. Memory hierarchy. Memory hierarchy includes main mem-ory and multiple levels of caches.

The cache is a small but fast memory that automatically keeps and manages copies of the most recently used and the most adjacent data from the. Control of complex systems is one of the fundamental problems in control theory. In this paper, a control method for complex systems modeled by a probabilistic Boolean network (PBN) is studied.

A PBN is widely used as a model of complex systems such as gene regulatory networks. For a PBN, the structural control problem is newly formulated. In this problem, a discrete probability Cited by: 5. Formal Techniques for the Verification and Optimal Control of Probabilistic Systems in the Presence of Modeling Uncertainties by We use Probabilistic Computation Tree Logic (PCTL) as the aiming to optimize a given system performance, while guar.

elicitation. Sec. 5 presents several novel applications of probabilistic integration for critical assessment2.

Sec. 6 concludes with an appraisal of the suitability of probabilistic numerical methods in the applied statistical context. 2 Background First we provide the reader with the relevant background. Sec. provides a formal descrip-Cited by: The study considers the model checkers ETMCC, MRMC, PRISM (sparse and hybrid mode), YMER and VESTA, and focuses on fully probabilistic systems.

Several of our experiments show significantly different run times and memory consumptions between the tools—up to various orders of magnitude—without, however, indicating a clearly dominating by: Control of complex systems is one of the fundamental problems in control theory.

In this paper, a control method for complex systems modeled by a probabilistic Boolean network (PBN) is studied. A PBN is widely used as a model of complex systems such as gene regulatory networks. For a PBN, the structural cont rol problem is newly formulated.

Probabilistic performance evaluation for multiclass 13 Fig. 5: (a)(b)(c) Posterior distribution of the dif ference of two posterior balanced accuracies com- puted as explained in Section aerial vehicles (UAVs) and other complex systems.

With advances in many fields of distributed computation including networking and multi-agent systems, there has been a proliferation of data capturing the behavior of com-putational complex systems.

These datasets are so large and inherently complex that they are impossible to examine : Andrew Fast. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the : Andris Ambainis.

Abstract. In this paper we show how quantitative program logic [] provides a formal framework in which to promote standard techniques of program analysis to a context where probability and nondeterminism interact, a situation common to probabilistic distributed show that overall performance can be formulated directly in the logic and that it can be derived from local Cited by: 3.

The variety and constraint can be generalized to a probabilistic framework, where they are replaced by entropy and information, respectively. Let us suppose that we do not know the precise state, s, of a system, but only the probability distribution, P(s), of the system to be in state generalization of the variety of the system can then be expressed as entropy H.Chapter 1 of my book on R software development, The Art of R Programming, NSP, ; Part of a VERY rough and partial draft of that book.

It is only about 50% complete, has various errors, and presents a number of topics differently from the .This book is intended as a (non-rigorous) introduction to machine learning, prob-abilistic graphical models and their applications. Formal proofs of theorems are generally omitted.

These notes are formed from the basis of lectures given to both undergraduate and graduate students at Aston University, Edinburgh University, and EPF Size: 3MB.