Examples of 'gaussian processes' in a sentence
Meaning of "gaussian processes"
Gaussian processes: In statistics, Gaussian processes are a collection of random variables, any finite number of which have a joint Gaussian distribution. They are used in machine learning and probabilistic modeling. ~ Context: Gaussian processes are commonly used in Bayesian optimization and machine learning algorithms
How to use "gaussian processes" in a sentence
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gaussian processes
Gaussian processes are the normally distributed stochastic processes.
I know that this is possible using gaussian processes.
A problem in Gaussian processes is the computational burden of the required covariance matrix inversion.
Modelling of complex signals using Gaussian processes.
Polar Gaussian processes are then interpreted via Sobol decomposition and generalized in higher dimensions.
This is the benefit of Gaussian processes.
Such Gaussian processes hold great promise.
The first one is based on simulations of Gaussian processes and is assessed rigorously.
We made the hypothesis that the studied networks are multivariate Gaussian processes.
Gaussian processes can be seen as an infinite-dimensional generalization of multivariate normal distributions.
This thesis is about stochastic integration with respect to Gaussian processes that are notsemimartingales.
Gaussian processes are thus useful as a powerful non-linear multivariate interpolation tool.
Modeling protein stability with Gaussian processes.
Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal.
Extremal theory for Gaussian processes.
See also
In fact, Gaussian processes can be seen as an infinite-dimensional generalization of multivariate normal distributions.
A review of Cox survival analysis using Gaussian processes.
Gaussian processes in biostatistics: a case study of personal emergency link usage in Hong Kong.
Gaussian vectors and Gaussian processes.
Gaussian processes (GPs) provide a flexible approach to construct probabilistic models for Bayesian data analysis.
Typical surrogate models for Bayesian optimization are Gaussian processes.
A non-parametric approach using Gaussian processes is also studied on this process.
Comparison of model selection criteria for Gaussian Processes.
Among the considered methods Gaussian Processes (GP) was selected for its superior predictive performance.
Usually, the models used to describe these processes are based on gaussian processes.
Abstract, Conditioning Gaussian processes ( GPs ) by inequality constraints gives more realistic models.
This thesis presents approaches in modelling non-stationarity from two different perspectives in Gaussian processes.
Modelling non-stationary functions with Gaussian processes.
In the third article, we consider general Volterra Gaussian processes.
GEM-SA - a program for performing sensitivity analysis with Gaussian processes.
Title of the talk, Self-intersection local time for Gaussian processes.
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