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315
Evaluating the Accuracy of SamplingBased Approaches to the Calculation of Posterior Moments
 IN BAYESIAN STATISTICS
, 1992
"... Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accurac ..."
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Cited by 604 (12 self)
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Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical
Posterior Moments Of The Cauchy Distribution
 In Maximum Entropy and Bayesian Methods
, 1998
"... The posterior moments of parameters specifying distributions are minimum mean square Bayesian estimators for the corresponding moments of those parameters, and as such are ubiquitous in the Bayesian approach to statistical inference of distributions. The Cauchy distribution is most notable for its w ..."
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Cited by 2 (2 self)
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The posterior moments of parameters specifying distributions are minimum mean square Bayesian estimators for the corresponding moments of those parameters, and as such are ubiquitous in the Bayesian approach to statistical inference of distributions. The Cauchy distribution is most notable for its
Journal of Econometrics 29 (1985) 318. NorthHolland POSTERIOR MOMENTS COMPUTED BY MIXED INTEGRATION*
"... A flexible numerical integration method is proposed for the computation of moments of a multivariate posterior density with different tail properties in different directions. The method (called mixed integration) amounts to a combination of classical numerical integration and Monte Carlo integration ..."
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A flexible numerical integration method is proposed for the computation of moments of a multivariate posterior density with different tail properties in different directions. The method (called mixed integration) amounts to a combination of classical numerical integration and Monte Carlo
Posterior regularization for structured latent variable models
 Journal of Machine Learning Research
, 2010
"... We present posterior regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of probabilistic models with latent variables. Posterior regularization separates model co ..."
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Cited by 138 (8 self)
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complexity from the complexity of structural constraints it is desired to satisfy. By directly imposing decomposable regularization on the posterior moments of latent variables during learning, we retain the computational efficiency of the unconstrained model while ensuring desired constraints hold
Posterior Momemts of the Cauchy Distribution
"... INTRODUCTION The Cauchy distribution [1][2] is given by the probability density function for the observation, on a line in some plane, of particles that are radiated randomly from an omnidirectional radiator at some position in the plane. If the line is the x axis and the radiator is at position ..."
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and the prior, . Since the observations are independent, . The use of the first posterior moment as an estimator for position is motivated by the fact that it is the minimum mean squared error (mmse) estimate for the mean of the distribution. It is straightforward to show that the posterior moment is the mmse
Distributed Bayesian Posterior Sampling via Moment Sharing
"... We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local p ..."
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Cited by 1 (1 self)
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partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among
Reinforcement learning with Gaussian processes
 In Proc. of the 22nd International Conference on Machine Learning
, 2005
"... Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framework by addressing two pressing issues, which were not adequately treated in the original GPTD paper (Engel et al., 2003). ..."
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Cited by 134 (11 self)
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the posterior moments of the value Gaussian process. We also present a SARSA based extension of GPTD, termed GPSARSA, that allows the selection of actions and the gradual improvement of policies without requiring a worldmodel.
An exact likelihood analysis of the multinomial probit model
, 1994
"... We develop new methods for conducting a finite sample, likelihoodbased analysis of the multinomial probit model. Using a variant of the Gibbs sampler, an algorithm is developed to draw from the exact posterior of the multinomial probit model with correlated errors. This approach avoids direct evalu ..."
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Cited by 166 (6 self)
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We develop new methods for conducting a finite sample, likelihoodbased analysis of the multinomial probit model. Using a variant of the Gibbs sampler, an algorithm is developed to draw from the exact posterior of the multinomial probit model with correlated errors. This approach avoids direct
Bayes inference in the Tobit censored regression model
 JOURNAL OF ECONOMETRICS 51 (1992) 7999. NORTHHOLLAND
, 1992
"... We consider the Bayes estimation of the Tobit censored regression model with normally distributed errors. A simple condition for the existence of posterior moments is provided. Suitable versions of Monte Carlo procedures based on symmetric multivariatet distributions, and Laplacian approximations i ..."
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Cited by 76 (6 self)
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We consider the Bayes estimation of the Tobit censored regression model with normally distributed errors. A simple condition for the existence of posterior moments is provided. Suitable versions of Monte Carlo procedures based on symmetric multivariatet distributions, and Laplacian approximations
Variable Selection and Model Comparison in Regression
, 1994
"... In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. In some cases any combination of variables may enter, but in others certain necessary conditions must be satisfied: e.g., in time se ..."
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Cited by 86 (2 self)
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series applications it is common to allow a lagged variable only if all shorter lags for the same variable also enter. This paper interprets this specification as a mixed continuousdiscrete prior distribution for coefficient values. It then utilizes a Gibbs sampler to construct posterior moments
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