[PDF] Top 20 Generic Inference in Latent Gaussian Process Models
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Generic Inference in Latent Gaussian Process Models
... the Gaussian process regression network ( gprn ) likelihood model of Wilson et ...nonlinear models where the correlation between the outputs can be spatially ...of latent Gaussian ... See full document
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Detecting Damage on Wind Turbine Bearings Using Acoustic Emissions and Gaussian Process Latent Variable Models
... Probabilistic PCA is a simple yet very useful model, with its simplicity coming from the fact that the map from X to Y is linear, and the observation noise is modelled as Gaussian. These are, however, two obvious ... See full document
9
GaussianProcesses jl:A Nonparametric Bayes package for the Julia Language
... applying Gaussian process models is the availability of well-developed open source software, which is available in many programming ...user-friendly Gaussian processes ...supporting ... See full document
23
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models
... the Gaussian process models affects the performance of the ...the models with MAP parameter values and re-ran the models and LOO tests, varying the length scales for all data sets ... See full document
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Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
... for inference in models with binomial ...the latent function f is propor- tional to a Gaussian density when conditioned on the aug- mented P´olya-Gamma ...mate inference algorithm in ... See full document
8
Gaussian process approximations for fast inference from infectious disease data
... the latent, or incubation, period of norovirus to be between ...the latent period of norovirus as ...the latent period, we therefore chose ω = 2 days − 1 as the largest value consistent with existing ... See full document
24
Bayesian forecasting of mortality rates by using latent Gaussian models
... Bayesian inference for the parameters of the dynamic Heligman–Pollard model requires a large amount of prior information whereas inference for the GMRF model is feasible with non-informative ... See full document
23
Assessing Approximate Inference for Binary Gaussian Process Classification
... zero-mean Gaussian prior as an ...dimension Gaussian most of the mass lies in a thin shell. For large latent signals, the likelihood essentially cuts off regions which are incompatible with the ... See full document
26
Efficient inference in multi task Cox process models
... log Gaussian Cox process ( lgcp ) framework to model multiple corre- lated point data ...of latent functions drawn from Gaus- sian process ...from Gaussian pro- cesses and can ... See full document
11
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
... graphical models provide general modelling tools for complex data, where it is natural to include assumptions on the data generating process by adding latent variables in the ...Such latent ... See full document
45
Linear Latent Force Models Using Gaussian Processes
... the latent force model idea and commented how it compares to pre- vious models in the machine learning and statistics ...the Gaussian process model associated to the outputs and different ... See full document
21
The correlation space of Gaussian latent tree models and model selection without fitting
... To pre-process the data, a smoothed cubic spline basis was fitted to each growth vector, result- ing in a set of functional data objects which were then regularly evaluated to obtain comparable discretized ... See full document
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Efficient State-Space Inference of Periodic Latent Force Models
... basis models (LBMs) have a long history in machine ...to inference using computationally efficient state-space ...spectrum Gaussian process regression method of L´ azaro-Gredilla et ... See full document
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Statistical Methods for Computer Experiments with Applications in Neuroscience
... Carlo inference for Bayesian online updating of Gaussian process regression and classification by particle learning ...a generic PL interface and supplies three types of correlation functions: ... See full document
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A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings
... topic inference of traditional LDA because it infers topics based on document-level co-occurrence of ...a latent concept topic model ...of latent concepts, which we introduce as latent ... See full document
7
Variational inference for latent variables and uncertain inputs in Gaussian processes
... the latent points means that our framework is generic and can be easily extended for multiple practical ...the latent points as noisy measurements of given inputs we obtain a method for ... See full document
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
... approximate inference. When the model under consideration is over a latent Gaussian field, with the approximation being Gaussian, we show how these approximations can systematically be ...of ... See full document
42
GPstuff: Bayesian Modeling with Gaussian Processes
... Gaussian process (GP) prior provides a flexible building block for many hierarchical Bayesian mod- els (Rasmussen and Williams, ...GP models and it has already been used in several published ... See full document
5
Latent feature models for large-scale link prediction
... class models assume that there is a number of clusters (or classes) underlying the observed entities and each entity belongs to certain ...block models [18] is a representative work that places a ... See full document
11
Approximate Marginals in Latent Gaussian Models
... approximate inference developed and studied mainly in the machine learning community, is then an obvious ...sparse Gaussian models with many latent ... See full document
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