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Questions tagged [gaussian-process]

26 questions
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Is it possible to train probabilistic model to return several distributions?

I have nonlinear data of function y(x), which is let's say parabolic. At some points of x there are several y's (look at the picture). Is it possible to train a probabilistic model to return several distributions (when needed) i.e. several means…
BatyaGG
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Gaussian process regressor returns almost identical std for all datapoints

I am using a Gaussian process regressor as the regressor for active learning and I use its standard deviation to choose the next training inctance (the one with the highest std is chosen). However, the std values returned by the regressor are almost…
Ash
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How do you choose a kernel for a discontinuous function in Gaussian Process Regression?

I'm doing Gaussian Process Regression and created a series of functions by gluing other functions together on random places. Here's an example: Perhaps this one is to complicated, but all the functions come from the same "family", they're all…
J. Dionisio
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1 answer

Gaussian Process for Classification: How to do predictions using MCMC methods

Problem I was reading about Gaussian Processes for regression in the "Gaussian Processes for Classification" textbook and in a few other online resources. Everywhere I look people seem to avoid talking about one would go about doing this. Can anyone…
Euler_Salter
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Why GP posterior variance is the worst-case error?(exact proof)

I am reading this paper, which explains the connecting idea Gaussian Process and Kernel methods in detail. I am impressed by the insightful explanation in this paper, but am stuck on one part in Chapter 3, Section 3.4 Error Estimates: Posterior…
eskim
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1 answer

How does bayesian optimization with gaussian processes work?

Could someone explain in simple words what are gaussian processes how does bayesian optimization work and their combination?
Ben
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Multivariate noise variance in Gaussian process prediction

In GP regression, we predict using $\mu^* = ... (K(X,X)+\sigma^2I)^{-1}...$ This is fine when the noise $\sigma$ is a scalar, but I am confused about what happens when $\sigma$ is Multivariate/anisotropic. $K(X,X) \in R^{m\times m}$, does $\sigma$'s…
Just_Alex
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Sequential sampling from Gaussian conditional not working

I'm trying to sequentially sample from a Gaussian Process prior. The problem is that the samples eventually converge to zero or diverge to infinity. I'm using the basic conditionals described e.g. here Note: the kernel(X,X) function returns the…
Jacob Holm
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Derivative of multi-output Gaussian Process

I am working on a project where I estimate transition and measurements models for a kalman filter using Gaussian Processes. In order to linearize the models I require the Jacobian of the estimated Guassian Process. For the single-output case this…
Michael
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GP derivative in GpyTorch

I am working on a project using GP-regression models to model transition and measurements models in a Kalman Filter. This means I need to be able to sample from the derivative of the original GP model. I am aware of how to combine the various…
Michael
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Using a trained classifier in an Android app

As the title suggests, I'm attempting to train some different classifiers into an android app. The main question I have is how to represent the different models in a neat and effective way, from python to Java (Android Studio). Background: I will…
Phil
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How exactly do Gaussian Processes (square dist kernel) enforce smoothness? (Aka how are they computed to do so?)

From: http://www.cs.cmu.edu/~16831-f12/notes/F10/16831_lecture22_jlisee/16831_lecture22.jlisee.pdf "Gaussian Processes artificially introduce correlation between close samples in that vector in order to enforce some sort of smoothness on the…
mHo2
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1 answer

Can a simple distance to a few nearest data points be used a measure of the uncertainty of a prediction?

One of the 'selling points' of the Gaussian process regression is that it provides not only the model but also the uncertainty estimate of a prediction. Then usually a picture is shown with a curve fitted to the data and a shaded area around it…
Vladislav Gladkikh
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How can I plot the covariance matrix of scikit-learn's Gaussian process kernel?

How can I plot the covariance matrix of a Gaussian process kernel built with scikit-learn? This is my code X = Buckling_masterset.reshape(-1, 1) y = E X_train, y_train = Buckling.reshape(-1, 1), E kernel = 1 * RBF(length_scale=1e1,…
Pedro Brandão
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VC dimension for Gaussian Process Regression

In neural networks, the VC dimension $d_{VC}$ equals approximately the number of parameters (weights) of the network. The rule of thump for good generalization is then $N \geq 10 d_{VC} \approx 10 * (\text{number of weigts})$. What is the VC…
kot
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