Additive Multivariate Gaussian Processes for Joint Species Distribution Modeling with Heterogeneous Data
نویسندگان
چکیده
منابع مشابه
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Sample code Thickness Unif. Cell Size Unif. Cell Shape Marginal Adhesion Epithelial Cell Size Bare Nuclei Bland Chromatin Normal Nucleoli Mitoses Class 1000025 5 1 1 1 2 1 3 1 1 Benign 1002945 5 4 4 5 7 10 3 2 1 Benign 1015425 3 1 1 1 2 2 3 1 1 Benign 1016277 6 8 8 1 3 4 3 7 1 Benign 1017023 4 1 1 3 2 1 3 1 1 Benign 1017122 8 10 10 8 7 10 9 7 1 Malignant 1018099 1 1 1 1 2 10 3 1 1 Benign 101856...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
Additive Gaussian Processes
We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesia...
متن کاملJoint segmentation of multivariate Gaussian processes using mixed linear models
The joint segmentation of multiple series is considered. A mixed linear model is used to account for both covariates and correlations between signals. An estimation algorithm based on EM which involves a new dynamic programming strategy for the segmentation step is proposed. The computational efficiency of this procedure is shown and its performance is assessed through simulation experiments. A...
متن کاملAdditive Gaussian Processes for Blending Gauge and Satellite Rainfall Data
Predicting ground rainfall from satellite estimates is useful as input for many applications, especially for areas with sparse rain gauges. We propose a predictive model based on an Additive Gaussian process (AGP) which can be viewed as the sum of a GP for the influence of the satellite estimate and a GP for the spatial distribution of rainfall between gauges. The hyperparameters for the covari...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bayesian Analysis
سال: 2020
ISSN: 1936-0975
DOI: 10.1214/19-ba1158