[PDF] Top 20 Robustness Guarantees for Bayesian Inference with Gaussian Processes

... 2017). Bayesian techniques, in particular, provide a principled way of combining a-priori information into the training process, so as to obtain an a-posteriori distribu- tion on test data, which also takes into ... See full document

... for Gaussian process regression with uncertain inputs (Girard et ...a Bayesian model for dynamical systems (Damianou et ...a Gaussian process prior on the latent space, X which is itself a function ... See full document

... We have examined models for stochastic volatility based on continuous super- positions of OU processes driven by pure jump L´evy processes. Such models are interesting as they can generate long memory, ... See full document

... discusses Bayesian inference for stochastic volatility models based on continuous superpositions of Ornstein-Uhlenbeck ...These processes represent an alternative to the previously considered ... See full document

... efficient Bayesian training of hierarchical Gaussian process ...other inference tasks, such as class conditional density estima- tion to further validate the ... See full document

... for Gaussian process regression with uncertain inputs (Girard et ...a Bayesian model for dynamical systems (Damianou et ...a Gaussian process prior on the latent space, X which is itself a function ... See full document

... GP inference agent into summary statistics, which is amenable to both efficient online update as well as multi-agent model fusion that exploits sparse connectiv- ity among agents for improving efficiency and ... See full document

... the robustness of the random walk Metropolis scheme: as the orientation of the posterior distribution of the two components θ 1 and θ 2 varies over the parameter space, it is necessary to rely ... See full document

... a Gaussian - shows that the qualities of inference for both approaches are similar, while the mean field algorithm is at least one order of magnitude ... See full document

... Gaussian process (GP) prior provides a flexible building block for many hierarchical Bayesian mod- els (Rasmussen and Williams, 2006). GPstuff (v4.1) is a versatile collection of computational tools for GP ... See full document

... To avoid explicitly selecting the number of mixtures, K, to use to model our input space, we may instead draw partitions from the Dirichlet process mixture model (DPMM) instead, as seen in Rasmussen and Ghahramani ... See full document

... many Bayesian MCMC strategies were developed with proven effectiveness and are acknowledged widely and openly as very promising tools for practical GW ...the Bayesian MCMC algorithm in a GW detection and ... See full document

... propose Bayesian inference for the Bingham distribution and they use developments in Bayesian computation for distributions with doubly intractable normalising constants (Møller et ... See full document

... To summarize, the presented work moves forward to a compact, consistent, and automatic mechanism via Bayesian formulation of the ABC to find an optimal kernel and its hyperparameters simultaneously. As can be seen ... See full document

... investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various ...of Gaussian covariance that make G-KL methods ... See full document

... In regards to sampling methods, the amount of research is not very extensive, most likely due to the high computational effort that is required to represent the response surface with a dedicated FE model. For example, ... See full document

... When necessary, the observed covariates have been log-transformed to reduce skewness and all of the covariates have been standardised prior to the analysis. This justifies using the same prior on all coefficients β in ... See full document

... In the last, we study their performance in terms of output SINR versus look direction error, and the result is shown in Fig. 6. It can be seen that the RB-FI-PC beamformer has the best robustness than others ... See full document

... on Gaussian Processes, a Bayesian non-linear learning method, to identify features that perform well on datasets for different language pairs and text domains, with translations produced by various ... See full document

... This paper employs a typical MCMC algorithm called the Metropolis–Hastings (M–H) algorithm (see Metropolis et al. [17]; Hastings [9]). The M–H (Algorithm 1) given below builds its Markov chain by accepting or rejecting ... See full document