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204
inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts
 Journal of Business and Economic Statistics
, 1993
"... We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved twostate indicator variable that follows a Markov process with unknown transition probabilities. A Bayesian framework is developed in which the unobserved s ..."
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Cited by 150 (5 self)
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We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved twostate indicator variable that follows a Markov process with unknown transition probabilities. A Bayesian framework is developed in which the unobserved
Bayesian inference via classes of normalized random measures
 ICER Working Papers  Applied Mathematics Series 52005, ICER  International Centre for Economic Research
, 2005
"... One of the main research areas in Bayesian Nonparametrics is the proposal and study of priors which generalize the Dirichlet process. Here we exploit theoretical properties of Poisson random measures in order to provide a comprehensive Bayesian analysis of random probabilities which are obtained by ..."
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Cited by 10 (2 self)
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which are generalizations of known efficient methods for the case of the Dirichlet process. We illustrate new examples of processes which can play the role of priors for Bayesian nonparametric inference and finally point out some interesting connections with the theory of generalized gamma convolutions
Performing bayesian inference by weighted model counting
 In Proceedings of the National Conference on Artificial Intelligence (AAAI
, 2005
"... Over the past decade general satisfiability testing algorithms have proven to be surprisingly effective at solving a wide variety of constraint satisfaction problem, such as planning and scheduling (Kautz and Selman 2003). Solving such NPcomplete tasks by “compilation to SAT ” has turned out to be a ..."
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Cited by 42 (0 self)
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. This paper begins to investigate the question of whether “compilation to modelcounting ” could be a practical technique for solving realworld #Pcomplete problems, in particular Bayesian inference. We describe an efficient translation from Bayesian networks to weighted model counting, extend the best model
Marginal PseudoLikelihood Inference for Markov Networks Marginal PseudoLikelihood Inference for Markov Networks
"... Since its introduction in the 1970’s, pseudolikelihood has become a wellestablished inference tool for random network models. More recently, there has been a revival of interest towards the pseudolikelihood based approach, motivated by several ’large p, small n ’ type applications. Under such ci ..."
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structures via harmonization of candidate sets of Markov blankets. The marginal pseudolikelihood method is shown to perform favorably against recent popular inference methods for Markov networks in terms of accuracy, while being at a comparable level in terms of computational complexity.
Estimation of copula models with discrete margins via Bayesian data augmentation
 Journal of the American Statistical Association
, 2012
"... Estimation of copula models with discrete margins can be difficult beyond the bivariate case. We show how this can be achieved by augmenting the likelihood with latent variables, and computing inference using the resulting augmented posterior. To evaluate this we propose two efficient Markov chain M ..."
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Cited by 9 (0 self)
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Estimation of copula models with discrete margins can be difficult beyond the bivariate case. We show how this can be achieved by augmenting the likelihood with latent variables, and computing inference using the resulting augmented posterior. To evaluate this we propose two efficient Markov chain
Bayesian Inference in Cumulative Distribution Fields
"... Abstract One approach for constructing copula functions is by multiplication. Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplication will result in a copula model, as discussed by Liebscher (J Mult Analysis, 2008). Parameterizing models vi ..."
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via products of CDFs has some advantages, both from the copula perspective (e.g., it is welldefined for any dimensionality) and from general multivariate analysis (e.g., it provides models where small dimensional marginal distributions can be easily readoff from the parameters). Independently
Marginally Specified Priors for Nonparametric Bayesian Estimation
, 2012
"... Prior specification for nonparametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. Realistically, a statistician is unlikely to have informed opinions about all aspects of such a parameter, but may have real infor ..."
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Cited by 1 (1 self)
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Prior specification for nonparametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. Realistically, a statistician is unlikely to have informed opinions about all aspects of such a parameter, but may have real
1 Likelihood ratios and Bayesian inference for Poisson channels
, 709
"... Abstract—In recent years, infinitedimensional methods have been introduced for the Gaussian channels estimation. The aim of this paper is to study the application of similar methods to Poisson channels. In particular we compute the Bayesian estimator of a Poisson channel using the likelihood ratio ..."
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and the discrete Malliavin gradient. This algorithm is suitable for numerical implementation via the MonteCarlo scheme. As an application we provide an new proof of the formula obtained recently in [5] relating some derivatives of the inputoutput mutual information of a timecontinuous Poisson channel
Bayesian Palaeoclimate Inference from Pollen in Southern Italy
"... We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate nonlinear nonGaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving ..."
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We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate nonlinear nonGaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving
Inference in Multilayer Networks via Large Deviation Bounds
 NIPS
, 1999
"... We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks does not preclude their effective use. We give algorithms for approximate probabilistic inference that exploit averaging ph ..."
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Cited by 8 (1 self)
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We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks does not preclude their effective use. We give algorithms for approximate probabilistic inference that exploit averaging
Results 1  10
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204