Examples of 'gaussian process' in a sentence
Meaning of "gaussian process"
gaussian process: In statistics and machine learning, a Gaussian process is a collection of random variables, any finite number of which have a joint Gaussian distribution. It is used for modeling and predicting outcomes based on historical data
How to use "gaussian process" in a sentence
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gaussian process
The limit is a gaussian process changed in time.
Gaussian process latent variable models.
Wide sense stationary gaussian process.
A gaussian process is applied to the masks.
Assessment of predictive relevance of covariates in Gaussian process models.
Sparse log Gaussian process in spatial epidemiology.
This procedure slightly restricts the properties of the Gaussian process.
The Gaussian process notoriously satisfies this condition.
We provide sufficient conditions for this convergence to hold to a limiting Gaussian process.
A Gaussian process can be used as a prior probability distribution over functions in Bayesian inference.
We will now examine the case of the energy of an arbitrary Gaussian Process.
This is not a Gaussian process.
A process according to a relationship K is a particular compound Gaussian process.
They involve a learning phase that consists in building a Gaussian process approximation of the performance function.
Gaussian process Gaussian smoothing.
See also
The thesis introduces two novel covariate selection methods for Gaussian process models.
In the geostatistics community Gaussian process regression is also known as Kriging.
Gaussian process is a powerful non-linear interpolation tool.
GEK can be interpreted as a form of Gaussian process regression.
However, Gaussian process has difficulty to deviate from its mean.
Then this is a Gaussian process.
When a parameterised kernel is used, optimisation software is typically used to fit a Gaussian process model.
Finally, we study a certain modelinvolving a Gaussian process and provide estimators for different parameters.
The Ornstein-Uhlenbeck process is a stationary Gaussian process.
Abstract, This thesis deals with the Gaussian process regression of two nested codes.
The former follows a lognormal process and the latter a mean-reverting Gaussian process.
Of a stationary Gaussian process.
Applications = = A Gaussian process can be used as a prior probability distribution over functions in Bayesian inference.
The at least one first classifier may comprise a Gaussian Process technique.
The other method is a Gaussian process model, which is a non-parametric Bayesian regression model.
Case of the energy of a White Gaussian Process.
Temporal and spatio-temporal Gaussian process models are useful in a multitude of data-intensive applications.
The values are originally chosen from a white gaussian process of variance 1.
This thesis develops Gaussian process (GP) models for Bayesian statistical inference in genetic epidemiology.
This is a so-called aggregating Gaussian process method.
Based on recent studies, Gaussian process regression (GPR) and Support vector regression (SVR) were selected.
For example, support vector machine regression systems, Gaussian process regression systems.
Let be a stationary mean-zero Gaussian process with covariances satisfying,.
Thus, it is feasible to model larger spatio-temporal datasets than with standard Gaussian process regression.
If the white noise formula 4 is a Gaussian process then formula 45 is also a Gaussian process.
By way of a non-limiting example, the machine learning method may implement a Gaussian Process Optimization.
Band-limited Gaussian process.
Ray-Knight type theorems relate the field It to an associated Gaussian process.
The main contribution is a coherent framework for LFI based on Gaussian process (GP) surrogate models.
The method according to claim 1, wherein the at least one first classifier ( 308 ) comprises a Gaussian Process.
Case of the energy of an arbitrary " subsampled " Gaussian process.
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