( 2014 ) Multiscale change point inference (with discussion) . J. R. Statist. Soc. B , 76 , 495 – 580 .Pein, F. and Munk, A. (2017). R package stepR: Multiscale change-point inference. Vi- gnette of R package stepR.
Multiscale change point inference We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE, for the change point problem in exponential family regression. An u... K Frick,AM And,H Sieling - 《Journal of the Royal Statistical Society》 被引量: 221发表: 2014年 ...
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single
Although the aforementioned models achieve state-of-the-art performance in terms of accuracy in an image classification task, they suffer from inefficient computation, slow training, and inference speed due to an extensive number of trainable parameters and floating point operations (FLOPs). Also, ...
Given the calibration data and the postulated model, the uncertainty in the model predictions can be obtained using an empirical Bayesian approach for model-based inference [115, 119]. In essence, these methods are computationally intensive methods that randomly walk within parameter space (i.e., ...
Therefore, it is anticipated that the dynamical flow decomposition based on SSH and the subsequent dynamical inference of interior flow structures can be applied to the world ocean. As such, 4D process-associated flow fields are recovered using solely SSH data. Figure 16 Open in figure viewer...
[6–8]. The former is limited by the need to be placed in a fixed location, and the inference of activity completely depends on the user’s interaction with these devices. For example, if the user is not within the sensor range or the object moves freely in the scene to introduce ...
[1,2,15], point cloud completion [16,17], layout inference [18], and point cloud registration [19–21]. The three main categories of 3D object classification based on the input to the deep learning network are volumetric representation [15,22], view-based [23] and raw point cloud ...
According to our resutls, SARS-CoV-2 exhibits a non-negligible positive change in modularity, like HPV type 16, Influenza A, and Bunyavirus. When analyzed from the perspective of Bayesian inference, we find a larger number of modules on average with respect to Louvain and an opposite trend:...
str Enhancing the performance and managing costs of UHPC has long been a focal point of existing studies. The pursuit of performance improvement involves the removal of coarse aggregates, incorporating advanced additives, using high-range water-reducing admixtures, and optimizing mix proportions and ...