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Author: Gauri Sankar Datta
Publisher: Springer Science & Business Media
ISBN: 146122036X
Category : Mathematics
Languages : en
Pages : 138
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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
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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: 1475555849
Category : Business & Economics
Languages : en
Pages : 18
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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: Michal Andrle
Publisher: International Monetary Fund
ISBN: 1484318374
Category : Business & Economics
Languages : en
Pages : 26
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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: G. M. Malliet
Publisher: Minotaur Books
ISBN: 1250092817
Category : Fiction
Languages : en
Pages : 302
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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
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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: Genshiro Kitagawa
Publisher: Springer Science & Business Media
ISBN: 1461207614
Category : Mathematics
Languages : en
Pages : 265
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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.
![The Poetical Works of Matthew Prior ... With the Life of the Author ... Embellished with Superb Engravings [including a Portrait]. PDF](https://books.google.com/books/content/images/frontcover/i5AT_SKHFzsC?fife=w80-h100)
Author: Matthew Prior
Publisher:
ISBN:
Category :
Languages : en
Pages : 258
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Book Description
Author: Matthew Prior
Publisher:
ISBN:
Category :
Languages : en
Pages : 350
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Book Description
Author: Matthew Prior
Publisher: Cambridge University Press
ISBN: 1107634490
Category : Literary Collections
Languages : en
Pages : 395
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Book Description
This 1905 volume contains the complete text of Prior's Poems on Several Occasions, taken from the folio of 1718.
Author: Matthew Prior
Publisher:
ISBN:
Category : English poetry
Languages : en
Pages : 500
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Book Description
