Bayesian Model for Fibre-Generated Point Processes 28
Fibre generated point processes and fields of orientations
... spatial point data clustered along or around a system of curves or ...these point-pattern data sets may not only facilitate a better understanding of how they arise but also aid reconstruction of missing ...
28
A gradient field approach to modelling fibre generated spatial point processes
... paper which introduced non-standard clustering ideas related to Diffusion Tensor Imaging (Su et al, 2008; Su, 2009). The work described briefly here is being written up for the PhD thesis of the first author (Hill, ...
5
An orientation field approach to modelling fibre generated spatial point processes
... Point patterns exhibiting a filamentary structure often arise in nature when events occur near some latent curvilinear generating feature. For example, earthquakes occur around seismic faults which lie on the ...
199
Bayesian Closed Point of Dispensing Planning Model
... for Bayesian decision model. Reports generated after each simulation run are used to give an insight about the number of people that entered and departed the system, the average time in the system ...
149
The effects of the model errors generated by discretization of “on-off” processes on VDA
... alized model of a partial differential equation with discon- tinuous “on-off” switches in the forcing term is adopted to demonstrate that the improper numerical treatment of “on- off” processes in the ...
12
A Bayesian space time model for discrete spread processes on a lattice
... bars generated from replicates simulated from 1000 draws of the posterior distributions of model parameters (λ i , ~ it ), dots indicate observed ...
48
On the use of model order reduction for simulating automated fibre placement processes
... as model parameter and then as problem extra-coordinate within the PGD ...incorporating model parameters, boundary condi- tions and geometrical ...computational point of view, they are performed ...
18
Bayesian wavelet approaches for parameter estimation and change point detection in long memory processes
... of model parameters has been widely investigated, little work has been done in designing methods for change point analysis of the long memory parameter in ARFIMA(p, d, q) ...for model parameters may ...
85
Point source moment tensor inversion through a Bayesian hierarchical model
... et al. 2012 ; Duputel et al. 2012 ), others compute the matrix from data residuals (e.g. Dettmer et al. 2007 ) or synthetically generated noise seismograms (e.g. Gouveia & Scales 1998 ; Sambridge 1999 ; Piana ...
13
Bayesian P splines and advanced computing in R for a changepoint analysis on spatio temporal point processes
... a Bayesian approach to detecting multiple unknown change- points over time in the inhomogeneous intensity of a spatio-temporal point pro- cess with spatial and temporal dependence within ...cess ...
21
A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems
... and point processes[6] was also proposed. Point processes were used before to predict the return time of users, where a self exciting point process is combined with a low rank ...
28
Bayesian Analysis of Spatial Point Patterns
... for point patterns, nor is there a widely applicable, easy-to-use method for model ...with point patterns, as shall be presented in this work, is that a point pattern contains limited ...
164
Approximation intensity for pairwise interaction Gibbs point processes using determinantal point processes
... hard-core model, see Figure 3 ...are generated using an exact algorithm; so the numerical results seem to be exact, except the slight bias induced by clipping the pattern ...
19
Cluster point processes on manifolds
... be generated in a natural way via certain manifold structures, such as the group action in the case of homogeneous manifolds and a metric structure for general Riemannian ...
50
Gibbs Sampling for Bayesian Prediction of SARMA Processes
... the Bayesian estimates of the model parameters and the Bayesian forecasts of the next 𝑘 future observations with the corresponding true values for Model ...the Bayesian estimates and ...
22
A copula model for marked point processes
... This model is the first we are aware of that address an important problem prevalent in the health ...The model we proposed in Section ?? is a fully parametric model and it would be desirable to relax ...
21
Bayesian Model Selection for Beta Autoregressive Processes
... a Bayesian estimation method that allows to estimate the parameters as well as the number of components of Beta autoregressive ...the model, while the moving average part will be object of future ...
35
Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes
... a Bayesian approach to obtaining uncondi- tional inference on structural features of the vector autoregressive model by means of evaluating posterior probabilities of alternative model speci…cations ...
39
Bayesian analysis of change point hazard rate model
... constant hazard rate change point model to a small data set of failure times of electrical.. insulation in Section 4.[r] ...
18
A Hierarchical Bayesian Language Model Based On Pitman Yor Processes
... coherent Bayesian probabilistic models, which have explicitly declared prior assumptions and whose merits can be argued in terms of how closely these fit in with the known properties of natural ...languages. ...
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