Download or read book Credibility Theory Linear Bayesian Estimation and the Kalman Filter written by Piet De Jong and published by . This book was released on 1981 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Course in Credibility Theory and its Applications written by Hans Bühlmann and published by Springer Science & Business Media. This book was released on 2005-08-30 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is ideal for practicing experts in particular actuaries in the field of property-casualty insurance, life insurance, reinsurance and insurance supervision, as well as teachers and students. It provides an exploration of Credibility Theory, covering most aspects of this topic from the simplest case to the most detailed dynamic model. The book closely examines the tasks an actuary encounters daily: estimation of loss ratios, claim frequencies and claim sizes.
Download or read book Bayesian Estimation and Tracking written by Anton J. Haug and published by John Wiley & Sons. This book was released on 2012-05-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB® toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.
Download or read book The Theory and Applications of Reliability With Emphasis on Bayesian and Nonparametric Methods written by Chris Tsokos and published by Elsevier. This book was released on 2012-12-02 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Theory and Applications of Reliability: With Emphasis on Bayesian and Nonparametric Methods, Volume I covers the proceedings of the conference on ""The Theory and Applications of Reliability with Emphasis on Bayesian and Nonparametric Methods."" The conference is organized so as to have technical presentations, a clinical session, and round table discussions. This volume is a 29-chapter text that specifically deals with the theoretical aspects of reliability estimation. Considerable chapters on the technical sessions are devoted to initial findings on the theory and applications of reliability estimation, with special emphasis on Bayesian and nonparametric methods. A Bayesian analysis implies the use of suitable prior information in association with Bayes theorem while the nonparametric approach analyzes the reliability components and systems under the assumption of a time-to-failure distribution with a wide defining property rather than a specific parametric class of probability distributions. The clinical session chapters discuss the actual problems encountered in reliability estimation. The remaining chapters deal with the status of the subject areas and the empirical Bayes developments. These chapters also present various probabilistic and statistic methods for reliability estimation. Theoreticians and reliability engineers will find this book invaluable.
Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Download or read book Robust Bayesian Estimation for the Linear Model and Robustizing the Kalman Filter written by Carl Johan Masreliez and published by . This book was released on 1974 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2023-05-31 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Download or read book Bayesian Analysis of Linear Models written by Broemeling and published by Routledge. This book was released on 2017-11-22 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.
Download or read book A Bayesian Method for Model Discrimination Using the Kalman Filter written by Paul George Wakim and published by . This book was released on 1983 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kalman Filtering Under Information Theoretic Criteria written by Badong Chen and published by Springer Nature. This book was released on 2023-09-19 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.
Download or read book Bayesian Estimation and Experimental Design in Linear Regression Models written by Jürgen Pilz and published by . This book was released on 1991-07-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.
Download or read book Bayesian Estimation of Discrete Signals with Local Dependencies written by Mohammad Hassan Majidi and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this thesis is to study the problem of data detection in wireless communication system, for both case of perfect and imperfect channel state information at the receiver. As well known, the complexity of MLSE being exponential in the channel memory and in the symbol alphabet cardinality is quickly unmanageable and forces to resort to sub-optimal approaches. Therefore, first we propose a new iterative equalizer when the channel is unknown at the transmitter and perfectly known at the receiver. This receiver is based on continuation approach, and exploits the idea of approaching an original optimization cost function by a sequence of more tractable functions and thus reduce the receiver's computational complexity. Second, in order to data detection under linear dynamic channel, when the channel is unknown at the receiver, the receiver must be able to perform joint equalization and channel estimation. In this way, we formulate a combined state-space model representation of the communication system. By this representation, we can use the Kalman filter as the best estimator for the channel parameters. The aim in this section is to motivate rigorously the introduction of the Kalman filter in the estimation of Markov sequences through Gaussian dynamical channels. By this we interpret and make clearer the underlying approximations in the heuristic approaches. Finally, if we consider more general approach for non linear dynamic channel, we can not use the Kalman filter as the best estimator. Here, we use switching state-space model (SSSM) as non linear state-space model. This model combines the hidden Markov model (HMM) and linear state-space model (LSSM). In order to channel estimation and data detection, the expectation and maximization (EM) procedure is used as the natural approach. In this way extended Kalman filter (EKF) and particle filters are avoided.
Download or read book Bayesian Reliability written by Michael S. Hamada and published by Springer Science & Business Media. This book was released on 2008-08-15 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses -- algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward. This book is primarily a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. This book can also be used as a textbook for a course in reliability and contains more than 160 exercises. Noteworthy highlights of the book include Bayesian approaches for the following: Goodness-of-fit and model selection methods Hierarchical models for reliability estimation Fault tree analysis methodology that supports data acquisition at all levels in the tree Bayesian networks in reliability analysis Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria Analysis of nondestructive and destructive degradation data Optimal design of reliability experiments Hierarchical reliability assurance testing
Download or read book Bayesian Statistics written by Source Wikipedia and published by University-Press.org. This book was released on 2013-09 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 83. Chapters: Bayesian probability, Prosecutor's fallacy, Likelihood function, Bayesian inference, Naive Bayes classifier, Bayesian network, Odds ratio, Variational Bayesian methods, Ensemble Kalman filter, Principle of maximum entropy, Bayesian spam filtering, Bayes estimator, Prior probability, Conjugate prior, Checking whether a coin is fair, Bayesian game, Imprecise probability, Data assimilation, Bayesian brain, Bayes factor, Graph cuts in computer vision, Jeffreys prior, Admissible decision rule, De Finetti's theorem, Bayesian inference in phylogeny, Maximum a posteriori estimation, Approximate Bayesian computation, Bayesian experimental design, Graphical model, Bayes linear statistics, Bayesian information criterion, Bayesian linear regression, Hierarchical Bayes model, Nested sampling algorithm, Evidence under Bayes theorem, Reference class problem, Recursive Bayesian estimation, Bayesian multivariate linear regression, Posterior probability, Credible interval, Extrapolation domain analysis, Hyperprior, Leonard Jimmie Savage, Deviance information criterion, AODE, Markov logic network, Bayesian search theory, Random naive Bayes, Bayesian average, A priori, Calibrated probability assessment, Hyperparameter, Gaussian process emulator, Marginal likelihood, GLUE, Aumann's agreement theorem, Precision, Base rate, Cromwell's rule, Speed prior, Bayesian econometrics, Expectation propagation, Strong prior, Sparse binary polynomial hashing, International Society for Bayesian Analysis.
Download or read book Bayesian Statistics in Actuarial Science written by Stuart A. Klugman and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: The debate between the proponents of "classical" and "Bayesian" statistica} methods continues unabated. It is not the purpose of the text to resolve those issues but rather to demonstrate that within the realm of actuarial science there are a number of problems that are particularly suited for Bayesian analysis. This has been apparent to actuaries for a long time, but the lack of adequate computing power and appropriate algorithms had led to the use of various approximations. The two greatest advantages to the actuary of the Bayesian approach are that the method is independent of the model and that interval estimates are as easy to obtain as point estimates. The former attribute means that once one learns how to analyze one problem, the solution to similar, but more complex, problems will be no more difficult. The second one takes on added significance as the actuary of today is expected to provide evidence concerning the quality of any estimates. While the examples are all actuarial in nature, the methods discussed are applicable to any structured estimation problem. In particular, statisticians will recognize that the basic credibility problem has the same setting as the random effects model from analysis of variance.
Download or read book A Linear Filtering Theory Approach to Recursive Credibility Estimation written by Benjamin Zehnwirth and published by . This book was released on 1983 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Encyclopedia of Actuarial Science 3 Volume Set written by Jozef L. Teugels and published by John Wiley & Sons. This book was released on 2004-10-29 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Actuarial Science presents a timely and comprehensive body of knowledge designed to serve as an essential reference for the actuarial profession and all related business and financial activities, as well as researchers and students in actuarial science and related areas. Drawing on the experience of leading international editors and authors from industry and academic research the encyclopedia provides an authoritative exposition of both quantitative methods and practical aspects of actuarial science and insurance. The cross-disciplinary nature of the work is reflected not only in its coverage of key concepts from business, economics, risk, probability theory and statistics but also by the inclusion of supporting topics such as demography, genetics, operations research and informatics.
