A Bayesian log-Gaussian Cox process model
Bayesian model based spatiotemporal survey designs and partially observed log Gaussian Cox process
... Our results show that in the presence of prior information on intensity function, survey designs that are expected to be most informative for partially observed LGCPs are different from traditional designs used for ...
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Bayesian log‐Gaussian Cox process regression: applications to meta‐analysis of neuroimaging working memory studies
... fully Bayesian random-effects meta-regression model based on log- Gaussian Cox processes, which can be used for meta-analysis of neuroimaging ...graphics process- ing units ...
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A Log Gaussian Cox process for predicting chimney fires at Fire Department Twente
... Poisson process could describe these emergency ...Poisson process did not cover the data ...the model to also encounter spatially dependent ...Poisson process is therefore extended with a ...
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Bayesian inference and data augmentation schemes for spatial, spatiotemporal and multivariate Log-Gaussian Cox processes in R
... spatial log-Gaussian Cox process is used to model incidences of murder in ...with log-Gaussian Cox processes is computational cost, although this issue is not ...
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Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
... variational Gaussian inference algorithms are available which can be applied to arbitrary link–functions, the choice of link–functions is not only crucial for defining the prior over intensities but can also be ...
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On the Bayesian treed multivariate Gaussian process with linear model of coregionalization.
... can model only a special case of non-stationarity since it does not allow for the spatial correlation to vary on ...multivariate model based on the Bayesian treed multivariate Gaussian ...
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Bayesian Inference on a Cox Process Associated with a Dirichlet Process
... a model of Cox process associated with a Dirichlet process was proposed with an emphasis on modeling spatial distri- butions of events generated by hidden ...Dirichlet process centered ...
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Modeling Tweet Arrival Times using Log Gaussian Cox Processes
... with log-Gaussian Cox pro- cess (LGCP), an inhomogeneous Poisson process (IPP) which models tweets to be generated by an underlying intensity function which varies across ...the model ...
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Analysis of multispecies point patterns by usingmultivariate log-Gaussian Cox processes
... point process models is mainly restricted to the bivariate case, see for example Diggle and Milne (1983); Harkness and Isham (1983); Högmander and Särkkä (1999); Brix and Møller (2001); Allard et ...
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Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators
... rank model parameters by their effect on outputs, and to quantify how uncertainty in parameters influences output ...of model runs are used to assess input-output ...using Gaussian process ...
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Interaction Modelling of inhomogeneous Poisson processes by means of log-Gaussian Cox processes
... distribution. Model comparison While we are able to compare the fits with the simulated examples, a user in practical applications cannot do ...appropriate model for the particular ...latent process ...
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Going off grid : computationally efficient inference for log Gaussian Cox processes
... for Bayesian inference on log- Gaussian Cox processes and to propose an approach that is much more computationally efficient based on continuously specified finite-dimensional Gaussian ...
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Gaussian process convolutions for Bayesian spatial classification
... Because the calculations were fast, it was reasonable to run the MCMC chain for a large number of iterations and then thin it heavily. It would be useful to find a more efficient sampler so that fewer samples are ...
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Application of Gaussian Process Priors on Bayesian Regression
... the model is mis-specified (Frlich, ...regression model, Li et al. (2016) used Gaussian process prior on the latent regression function, while they used generalized extreme value (GEV) link ...
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Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.
... of model will never be known with certainty, and so it is impor- tant to characterise the effect of this uncertainty on model outputs, and on conclusions drawn from ...of model parameters on ...
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Careful prior specification avoids incautious inference for log Gaussian Cox point processes
... given log-Gaussian Cox model, this trade-off is governed by the hyperprior choices for the precision of the included random field ...structured model ensures that given hyperprior ...
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Careful prior specification avoids incautious inference for log-Gaussian Cox point processes
... point process methodology (Diggle, 2003; Illian et ...a Gaussian random field, which accounts for spatial structures unexplained by the covariates (Illian et ...
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Correction: Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.
... In this supporting information, we include details of the mathematics that underpin the approach taken in this study. A much more in-depth coverage of Gaussian process (GP) emulators is given in the MUCM ...
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Vibration-based Bayesian model updating of civil engineering structures applying Gaussian process metamodel
... For Peer Review 1 study is the first to use the MBA to update the initial FEM of a real structure 2 for two states—undamaged and damaged conditions—in which the 3 damaged state represents changes in structural parameters ...
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Bayesian Filtering with Online Gaussian Process Latent Variable Models
... to Bayesian filtering, where the predic- tion and observation models are learned in an online ...mixture model representation with Gaussian Processes (GP) based ...mation process, we explore ...
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