Identification And Other Probabilistic Models
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Identification And Other Probabilistic Models
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Author : Rudolf Ahlswede
language : en
Publisher: Springer Nature
Release Date : 2021-06-22
Identification And Other Probabilistic Models written by Rudolf Ahlswede and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-22 with Mathematics categories.
The sixth volume of Rudolf Ahlswede's lectures on Information Theory is focused on Identification Theory. In contrast to Shannon's classical coding scheme for the transmission of a message over a noisy channel, in the theory of identification the decoder is not really interested in what the received message is, but only in deciding whether a message, which is of special interest to him, has been sent or not. There are also algorithmic problems where it is not necessary to calculate the solution, but only to check whether a certain given answer is correct. Depending on the problem, this answer might be much easier to give than finding the solution. ``Easier'' in this context means using fewer resources like channel usage, computing time or storage space. Ahlswede and Dueck's main result was that, in contrast to transmission problems, where the possible code sizes grow exponentially fast with block length, the size of identification codes will grow doubly exponentially fast. The theory of identification has now developed into a sophisticated mathematical discipline with many branches and facets, forming part of the Post Shannon theory in which Ahlswede was one of the leading experts. New discoveries in this theory are motivated both by concrete engineering problems and by explorations of the inherent properties of the mathematical structures. Rudolf Ahlswede wrote: It seems that the whole body of present day Information Theory will undergo serious revisions and some dramatic expansions. In this book we will open several directions of future research and start the mathematical description of communication models in great generality. For some specific problems we provide solutions or ideas for their solutions. The lectures presented in this work, which consists of 10 volumes, are suitable for graduate students in Mathematics, and also for those working in Theoretical Computer Science, Physics, and Electrical Engineering with a background in basic Mathematics. The lectures can be used as the basis for courses or to supplement courses in many ways. Ph.D. students will also find research problems, often with conjectures, that offer potential subjects for a thesis. More advanced researchers may find questions which form the basis of entire research programs. The book also contains an afterword by Gunter Dueck.
Identification And Other Probabilistic Models
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Author : Rudolf Ahlswede
language : en
Publisher:
Release Date : 2021
Identification And Other Probabilistic Models written by Rudolf Ahlswede and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The sixth volume of Rudolf Ahlswede's lectures on Information Theory is focused on Identification Theory. In contrast to Shannon's classical coding scheme for the transmission of a message over a noisy channel, in the theory of identification the decoder is not really interested in what the received message is, but only in deciding whether a message, which is of special interest to him, has been sent or not. There are also algorithmic problems where it is not necessary to calculate the solution, but only to check whether a certain given answer is correct. Depending on the problem, this answer might be much easier to give than finding the solution. ``Easier'' in this context means using fewer resources like channel usage, computing time or storage space. Ahlswede and Dueck's main result was that, in contrast to transmission problems, where the possible code sizes grow exponentially fast with block length, the size of identification codes will grow doubly exponentially fast. The theory of identification has now developed into a sophisticated mathematical discipline with many branches and facets, forming part of the Post Shannon theory in which Ahlswede was one of the leading experts. New discoveries in this theory are motivated both by concrete engineering problems and by explorations of the inherent properties of the mathematical structures. Rudolf Ahlswede wrote: It seems that the whole body of present day Information Theory will undergo serious revisions and some dramatic expansions. In this book we will open several directions of future research and start the mathematical description of communication models in great generality. For some specific problems we provide solutions or ideas for their solutions. The lectures presented in this work, which consists of 10 volumes, are suitable for graduate students in Mathematics, and also for those working in Theoretical Computer Science, Physics, and Electrical Engineering with a background in basic Mathematics. The lectures can be used as the basis for courses or to supplement courses in many ways. Ph.D. students will also find research problems, often with conjectures, that offer potential subjects for a thesis. More advanced researchers may find questions which form the basis of entire research programs. The book also contains an afterword by Gunter Dueck.
Probabilistic Models For The Identification And Interpretation Of Somatic Single Nucleotide Variants In Cancer Genomes
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Author : Andrew Justin Latham Roth
language : en
Publisher:
Release Date : 2015
Probabilistic Models For The Identification And Interpretation Of Somatic Single Nucleotide Variants In Cancer Genomes written by Andrew Justin Latham Roth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Novelty Detection For Multivariate Data Streams With Probabilistic Models
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Author : Christian Gruhl
language : de
Publisher: BoD – Books on Demand
Release Date : 2022-01-01
Novelty Detection For Multivariate Data Streams With Probabilistic Models written by Christian Gruhl and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Technology & Engineering categories.
The autonomous detection of unexpected changes in data is called novelty detection. Multivariate data streams consisting of measurements from multiple sensors often form the basis to detect such changes. Specific examples of such changes are, for instance, cardiac arrhythmias, power failures, storms or network attacks. Accordingly, changes can affect both a system itself and the environment in which it is embedded. This doctoral thesis investigates methods for online novelty detection in multivariate data streams and presents the CANDIES methodology. A unique feature of this method is the explicit separation of the input space of a probabilistic model into different regions – High-Density Regions (HDR) and Low-Density Regions (LDR) – with detection techniques specifically designed for each. While other detectors can usually only detect novelties or anomalies in LDR, the CANDIES method can also identify novelties in HDR. It also offers possibilities to handle concept drift and noise in data streams. Another distinctive feature of CANDIES is the notion of novelties as an agglomeration of anomalies that have a certain relation to each other (spatially or temporally). Additionally, the focus of this work is also on the experimental evaluation of novelty detection algorithms in general. For this purpose, a data generator that can synthesise data streams and novelties is presented, and a new evaluation measure, the FDS, is specifically designed to evaluate novelty detection methods. All methods, algorithms and tools developed and used in this thesis are also publicly and freely available online.
Detection Method And Probabilistic Models
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Author : W. K. ESTES
language : en
Publisher:
Release Date : 1964
Detection Method And Probabilistic Models written by W. K. ESTES and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1964 with categories.
Understanding Probability Models
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Author : Carlos Narciso Bouza Herrera
language : en
Publisher: Nova Science Publishers
Release Date : 2020-03
Understanding Probability Models written by Carlos Narciso Bouza Herrera and has been published by Nova Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03 with Probabilities categories.
This book intends to highlight how the Theory of Probability supports, not only statistical modeling but how it allows describing different real life phenomena. It gives clues for understanding the philosophic roots of probability and how they are present in different areas of knowledge. The readers may use the book as a source for understanding the philosophical development of probability concepts and of the intents to obtain mathematical models. The chapters deal with the understanding of how probability models are usable for determining: â¢A Probabilistic model of the best flight value for the design on paper of a helicopter â¢How to model the improvement of the behavior of water heating systems and of the reliability of systems â¢Models for determining the probability of non responses in inquiries and to evaluate the missing data. â¢The modeling of various problems related with the behavior of ordering models of use in decision rules and of general properties of Order Statistics. â¢A unified study of the probabilistic aspects of two Metaheuristics: Simulated Annealing and Tabu Search. â¢How to obtain the identification of econometric techniques for dealing efficiently with the study of economic growth models under endogeneity. This book will be of interest for biometricians, statisticians, economists, engineers dealing with control and reliability, as well for informaticians.
Analyzing Risk Through Probabilistic Modeling In Operations Research
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Author : Jakóbczak, Dariusz Jacek
language : en
Publisher: IGI Global
Release Date : 2015-11-03
Analyzing Risk Through Probabilistic Modeling In Operations Research written by Jakóbczak, Dariusz Jacek and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-03 with Business & Economics categories.
Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.
Risk Management Technologies
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Author : E.D. Solozhentsev
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-04-27
Risk Management Technologies written by E.D. Solozhentsev and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-27 with Technology & Engineering categories.
This book presents intellectual, innovative, information technologies (I3-technologies) based on logical and probabilistic (LP) risk models. The technologies presented here consider such models for structurally complex systems and processes with logical links and with random events in economics and technology. The volume describes the following components of risk management technologies: LP-calculus; classes of LP-models of risk and efficiency; procedures for different classes; special software for different classes; examples of applications; methods for the estimation of probabilities of events based on expert information. Also described are a variety of training courses in these topics. The classes of risk models treated here are: LP-modeling, LP-classification, LP-efficiency, and LP-forecasting. Particular attention is paid to LP-models of risk of failure to resolve difficult economic and technical problems. Amongst the discussed procedures of I3-technologies are the construction of LP-models, LP-identification of risk models; LP-risk analysis, LP-management and LP-forecasting of risk. The book further considers LP-models of risk of invalidity of systems and processes in accordance with the requirements of ISO 9001-2008, LP-models of bank operational risks in accordance with the requirements of Basel-2, complex risk LP-models for preventing ammunition depot explosions, enterprise electric power supply systems, debugging tests of technical systems, etc. The book also considers LP-models of credit risks, securities portfolios, operational risks in banking, conteraction of bribes and corruption, etc. A number of applications is given to show the effectiveness of risk management technologies. In addition, topics of lectures and practical computer exercises intended for a two-semester course “Risk management technologies” are suggested.
A Probabilistic Model Of The Genotype Phenotype Relationship
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Author : Jean-Pierre Hugot
language : en
Publisher: CRC Press
Release Date : 2018-06-19
A Probabilistic Model Of The Genotype Phenotype Relationship written by Jean-Pierre Hugot and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-19 with Science categories.
A Probabilistic Model of the Genotype/Phenotype Relationship provides a new hypothesis on the relationship between genotype and phenotype. The main idea of the book is that this relationship is probabilistic, in other words, the genotype does not fully explain the phenotype. This idea is developed and discussed using the current knowledge on complex genetic diseases, phenotypic plasticity, canalization and others.
Probabilistic Models
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Author : Source Wikipedia
language : en
Publisher: Booksllc.Net
Release Date : 2013-09
Probabilistic Models written by Source Wikipedia and has been published by Booksllc.Net this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09 with categories.
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 26. Chapters: Bayesian brain, Binary Independence Model, Constellation model, Continuum structure function, Divergence-from-randomness model, Factored language model, First-order reliability method, Generative model, Latent Dirichlet allocation, Maier's theorem, Mixture model, N-gram, Probabilistic automaton, Probabilistic relational model, Probabilistic relational programming language, Probabilistic relevance model, Probabilistic voting model, Stochastic context-free grammar, Stochastic grammar, Voter model. Excerpt: In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data-set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population-identity information. Some ways of implementing mixture models involve steps that attribute postulated sub-population-identities to individual observations (or weights towards such sub-populations), in which case these can be regarded as types of unsupervised learning or clustering procedures. However not all inference procedures involve such steps. Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). A typical finite-dimensional mixture model is a hierarchical model consisting...