Results 1  10
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3,943
A Characterization of Virtual Bayesian Implementation
, 2005
"... We provide a characterization of virtual Bayesian implementation in pure strategies for environments satisfying nototalindifference. A social choice function in such environments is virtually Bayesian implementable if and only if it satisfies incentive compatibility and a condition we term virtua ..."
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Cited by 11 (5 self)
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We provide a characterization of virtual Bayesian implementation in pure strategies for environments satisfying nototalindifference. A social choice function in such environments is virtually Bayesian implementable if and only if it satisfies incentive compatibility and a condition we term
Justifiability of Bayesian Implementation in
"... We show that in oligopolistic markets the social choice correspondence which selects all socially efficient outcomes is Nash implementable if the number of firms is at least two. Thus, monopoly regulation whenever consumers are favored by the designer or the society is the only framework, among all ..."
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We show that in oligopolistic markets the social choice correspondence which selects all socially efficient outcomes is Nash implementable if the number of firms is at least two. Thus, monopoly regulation whenever consumers are favored by the designer or the society is the only framework, among all
Type Diversity and Virtual Bayesian Implementation
, 2001
"... It is well known that a social choice function is truthfully implementable in Bayesian Nash equilibrium if and only if it is incentive compatible. However, in general it is not possible to rule out other equilibrium outcomes, and additional conditions, e.g., Bayesian monotonicity, are needed to en ..."
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It is well known that a social choice function is truthfully implementable in Bayesian Nash equilibrium if and only if it is incentive compatible. However, in general it is not possible to rule out other equilibrium outcomes, and additional conditions, e.g., Bayesian monotonicity, are needed
Bayesian implementation with partially honest individuals
, 2012
"... Abstract We study Bayesian implementation when all individuals are partially honest. We show that with this assumption incentive compatibility and no veto power are together sufficient for full implementation without any further restrictions on the environment. This generalizes the result of JEL C ..."
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Cited by 1 (0 self)
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Abstract We study Bayesian implementation when all individuals are partially honest. We show that with this assumption incentive compatibility and no veto power are together sufficient for full implementation without any further restrictions on the environment. This generalizes the result of JEL
Bayesian Implementable Efficient and Core Allocations
, 2000
"... I examine the implementation of core allocations when agents are differently informed. A one state deviation principle (an allocation cannot be improved at any state) and measurability restrictions (blocking allocations may only be measurable with respect to each agent's private information) ar ..."
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Cited by 4 (0 self)
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) are sufficient to yield interim core solutions that are Bayesian implementable. Private measurability of blocking allocations is necessary for implementation. Similar results hold for interim efficiency. However, the results cannot be extended to exclusive information environments.
Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman & Donald B. Rubin, and Robert M. Hauser, and a Rejoinder)
 SOCIOLOGICAL METHODOLOGY 1995, EDITED BY PETER V. MARSDEN, CAMBRIDGE,; MASS.: BLACKWELLS.
, 1995
"... It is argued that Pvalues and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a singl ..."
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Cited by 585 (21 self)
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single model, they ignore model uncertainty and so underestimate the uncertainty about quantities of interest. The Bayesian approach to hypothesis testing, model selection and accounting for model uncertainty is presented. Implementing this is straightforward using the simple and accurate BIC
A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized tTest and Statistical Inferences of Gene Changes
 Bioinformatics
, 2001
"... Motivation: DNA microarrays are now capable of providing genomewide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory ..."
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Cited by 491 (6 self)
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due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data. Results: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model logexpression values by independent normal
Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder r ..."
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Cited by 1194 (81 self)
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probability labels on each definition and nearmaximal Bayes posterior probability and then 2) alters the probability labels to further increase the posterior probability. Stage 1) is implemented within CProgol4.5, which differs from previous versions of Progol by allowing userdefined evaluation
WinBUGS  a Bayesian modelling framework: concepts, structure, and extensibility
 STATISTICS AND COMPUTING
, 2000
"... WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an ob ..."
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Cited by 430 (6 self)
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WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannon
Results 1  10
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3,943