Bayesian inference
Robustness Guarantees for Bayesian Inference with Gaussian Processes
... of Bayesian inference with Gaussian process priors with respect to adversarial examples and invariance ...of Bayesian models in safety-critical ...
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Collapsed Variational Bayesian Inference for PCFGs
... variational Bayesian inference (CVB) algorithm for ...variational Bayesian inference, but offers almost the same performance as the stochastic al- gorithms due to its weak ...
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Computational Methods for Bayesian Inference in Complex Systems
... solve Bayesian inference problems on dis- tributed high performance computing (HPC) resources and to take advantage of new computing environments like ...solve inference problems in ...
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Bayesian inference on non stationary data
... conducting Bayesian inference on the presence o f seasonal and zero frequency unit roots in quarterly data The main technique used is the analysis of posterior odds ratios A new parameterisation is provided ...
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Bayesian inference with monotone instrumental variables
... a Bayesian inference on the distribution of bounds under the MIV assump- ...the Bayesian inference method to other identification problems such as those in Kreider and Pepper ...
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Bayesian inference for double Pareto lognormal queues
... One point to note however is that, as commented in Wiper (1997), it can be shown that the predictive means of the equilibrium queue size and waiting time distributions do not exist. This is a typical feature for ...
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Isoseparation and robustness in finite parameter Bayesian inference
... Under a new family of separations the distance between two poste- rior densities is the same as the distance between their prior densities whatever the observed likelihood when that likelihood is strictly pos- itive. ...
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Bayesian Inference for Finite State Transducers
... a Bayesian inference algorithm that can be used to train any cascade of weighted finite-state transducers on end-to- end ...previous Bayesian ap- ...
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A Poisson process reparameterisation for Bayesian inference for extremes
... on Bayesian inference but the reparameterisations we find can be used to improve likelihood inference as well, simply by ignoring the prior ...
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Exact Bayesian inference for the Bingham distribution
... exact Bayesian inference for the Bingham distribution which has been a difficult task so ...fully Bayesian approach has the benefit of providing an honest assessment of the uncertainty of the ...
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Crafting a Lightweight Bayesian Inference Engine
... on Bayesian inference to perform probabilistic inferences and provide quantitative supports for decisions, hypotheses, forecasts and ...probability-based inference engine that is domain independent ...
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Stochastic Gradient Descent as Approximate Bayesian Inference
... As a simple example, consider SGD with a constant learning rate (constant SGD). Constant SGD first marches toward an optimum of the objective function and then bounces around its vicinity. (In contrast, traditional SGD ...
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Bayesian Inference for Mixtures of Stable Distributions.
... The existence of simulation methods for stable distributions opens the way to Bayesian inference on the parameters of this distribution family. In this section we define a stable random variable and briefly ...
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Bayesian inference in a cointegrating panel data model
... Abstract: This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is ß exible ...
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Bayesian Inference for Zodiac and Other Homophonic Ciphers
... novel Bayesian approach for deciphering complex substitution ...dictionaries. Bayesian inference is performed on our model using an efficient sampling ...the Bayesian deci- pherment output on ...
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Bayesian Inference from Symplectic Geometric Viewpoint
... to Bayesian inference from symplectic-contact geometric viewpoint due to Mori [5] ...gives Bayesian updating for mean and variance in univariate ...a Bayesian updating for covariant matrices ...
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BayesPy: Variational Bayesian Inference in Python
... variational Bayesian update equations, the user can construct models faster and in a less error-prone ...efficient inference make BayesPy suitable for both average and expert Bayesian ...
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Advances in Bayesian Inference for Species Divergence Times.
... Owing to the complexity of the process of molecular evolution, none of the above three hypotheses could completely account for patterns of rate heterogeneity among lineages. These hypotheses are not mutually exclusive. ...
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Semiparametric Bayesian inference in multiple equation models
... The advantages of remaining within the framework of the Normal linear regression model with a natural conjugate prior are clear. This model is very well understood and standard textbook results for estima- tion, model ...
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Bayesian Inference in Spatial Sample Selection Models
... the Bayesian estimator in all algorithms reports estimates that are close to the true parameter value for the autoregressive parameter of the selection ...the Bayesian estimator in Algorithms ...the ...
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