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Author: Gauri Sankar Datta Publisher: Springer Science & Business Media ISBN: 146122036X Category : Mathematics Languages : en Pages : 138
Book Description
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Author: Gauri Sankar Datta Publisher: Springer Science & Business Media ISBN: 146122036X Category : Mathematics Languages : en Pages : 138
Book Description
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Author: Michal Andrle Publisher: International Monetary Fund ISBN: 1484318374 Category : Business & Economics Languages : en Pages : 26
Book Description
This paper proposes a novel way of formulating priors for estimating economic models. System priors are priors about the model's features and behavior as a system, such as the sacrifice ratio or the maximum duration of response of inflation to a particular shock, for instance. System priors represent a very transparent and economically meaningful way of formulating priors about parameters, without the unintended consequences of independent priors about individual parameters. System priors may complement or also substitute for independent marginal priors. The new philosophy of formulating priors is motivated, explained and illustrated using a structural model for monetary policy.
Author: Michal Andrle Publisher: International Monetary Fund ISBN: 1475555849 Category : Business & Economics Languages : en Pages : 18
Book Description
The paper introduces “system priors”, their use in Bayesian analysis of econometric time series, and provides a simple and illustrative application. System priors were devised by Andrle and Benes (2013) as a tool to incorporate prior knowledge into an economic model. Unlike priors about individual parameters, system priors offer a simple and efficient way of formulating well-defined and economically-meaningful priors about high-level model properties. The generality of system priors are illustrated using an AR(2) process with a prior that most of its dynamics comes from business-cycle frequencies.
Author: Genshiro Kitagawa Publisher: Springer Science & Business Media ISBN: 1461207614 Category : Mathematics Languages : en Pages : 265
Book Description
Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.
Author: G. M. Malliet Publisher: Minotaur Books ISBN: 1250092817 Category : Fiction Languages : en Pages : 302
Book Description
“G. M. Malliet has crafted the English village of our dreams.” —Charlaine Harris Agatha Award-winning author G. M. Malliet has charmed mystery lovers and cozy fans with her critically acclaimed mysteries. In Prior's Wood, featuring handsome spy-turned-cleric Max Tudor, won’t disappoint. Newly returned from investigating a murder in Monkslip-super-Mare, handsome Max Tudor wants nothing more than to settle back into his predictable routine as vicar of St. Edwold’s Church in the village of Nether Monkslip. But the flow of his sermon on Bathsheba is interrupted when the lady of the local manor house is found in a suicide pact with her young lover. Lady Duxter’s husband rallies quickly from the double tragedy—too quickly, it is murmured in the village. Lord Duxter already has offered his manor house to a motley crew of writers, including Max’s wife Awena, for his writers’ retreat, and he insists the show must go on. When a young girl goes missing and a crime writer becomes a target, DCI Cotton asks Max to lend his MI5 expertise to the investigation. Many suspects emerge as the scope of the investigation widens beyond the writers to villagers who had crossed swords with the insufferably smug crime author. But Max begins to wonder: was the attack on the writer only part of a broader conspiracy of silence?
Author: Raphael Urs Keusch Publisher: BoD – Books on Demand ISBN: 3866287682 Category : Computers Languages : en Pages : 275
Book Description
Normal with unknown variance (NUV) priors are a central idea of sparse Bayesian learning and allow variational representations of non-Gaussian priors. More specifically, such variational representations can be seen as parameterized Gaussians, wherein the parameters are generally unknown. The advantage is apparent: for fixed parameters, NUV priors are Gaussian, and hence computationally compatible with Gaussian models. Moreover, working with (linear-)Gaussian models is particularly attractive since the Gaussian distribution is closed under affine transformations, marginalization, and conditioning. Interestingly, the variational representation proves to be rather universal than restrictive: many common sparsity-promoting priors (among them, in particular, the Laplace prior) can be represented in this manner. In estimation problems, parameters or variables of the underlying model are often subject to constraints (e.g., discrete-level constraints). Such constraints cannot adequately be represented by linear-Gaussian models and generally require special treatment. To handle such constraints within a linear-Gaussian setting, we extend the idea of NUV priors beyond its original use for sparsity. In particular, we study compositions of existing NUV priors, referred to as composite NUV priors, and show that many commonly used model constraints can be represented in this way.
Author: Evelyn Hood Publisher: Severn House Publishers Ltd ISBN: 1780104979 Category : Fiction Languages : en Pages : 224
Book Description
When Tricia and Derek Borland bring home their baby daughter, their elderly neighbours are more than willing to help the new mother. But their enthusiasm begins to wane when it becomes apparent that Tricia is more interested in going out with her friends than looking after her new baby - and is only too ready to take advantage of their kindness. Meanwhile, Lewis Ralston-Kerr and his fiancé Ginny are horrified by Ginny's flamboyant mother's determination to sweep aside their desire for a small village wedding and organise a large society affair. What's more, the Prior's Ford Progress Committee have decided that the traditional village summer festival needs pepping up this year ...
Author: Joseph J.K. O Ruanaidh Publisher: Springer Science & Business Media ISBN: 1461207177 Category : Computers Languages : en Pages : 256
Book Description
This book is concerned with the processing of signals that have been sam pled and digitized. The fundamental theory behind Digital Signal Process ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87]. The term "Digital Signal Processing", in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing would usually contain detailed de scriptions of the standard mathematical machinery often used to describe signals. It would also motivate an approach to real world problems based on concepts and results developed in linear systems theory, that make use of some rather interesting properties of the time and frequency domain representations of signals. While this book assumes some familiarity with traditional methods the emphasis is altogether quite different. The aim is to describe general methods for carrying out optimal signal processing.
Author: Geoffrey McLachlan Publisher: John Wiley & Sons ISBN: 047165406X Category : Mathematics Languages : en Pages : 419
Book Description
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Author: Gauri Sankar Datta
Author: Gauri Sankar Datta
Author: Michal Andrle
Author: Michal Andrle
Author: Genshiro Kitagawa
Author: G. M. Malliet
Author: Raphael Urs Keusch
Author: Evelyn Hood
Author: Cain Mckay
Author: Joseph J.K. O Ruanaidh
Author: Geoffrey McLachlan