# Questions tagged [gaussian-process]

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### Variational Learning of Inducing Variables in Sparse Gaussian Processes

In this paper - Variational Learning of Inducing Variables in Sparse Gaussian Processes After equation (5), the statement: Here, $p(\textbf{f}|\textbf{f}_m) = p(\textbf{f}|\textbf{f}_m, \textbf{y})$ ...
21 views

### How do I automatically evaluate an objective_plot after BayesSearchCV to find the *theoretical* optimal model?

I did a hyper optimization for a XGBClassifier using BayesSearchCV. I increased the kappa ...
99 views

### Understanding the uncertainty in gaussian processes

Consider the following image: which is an fitted GP. Note how $0 <= x <= 2$ yield a much higher uncertainty than e.g $5 <= x <= 8$. Thus gps are good when dealing with the exploration vs ...
16 views

### How to improve computational performances in GaussianProcessRegressor?

I need to fit my GaussianProcessRegressor with a lot of data. In particular, I start fitting the GP with few data, and I add more at each step. Since I need to ...
147 views

### On the time complexity of Bayesian linear regression and Gaussian process

By drawing analogy, I believe that Bayesian linear regression has a time complexity same to standard linear regression $𝑂(𝑛𝑝^2+𝑝^3)$ which is dominated by the number of features $p$ (What is the ...
1 vote
23 views

### 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 ...
1 vote
390 views

### 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 ...
20 views

### 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 ...
1 vote
220 views

### 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?
130 views

1 vote
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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. ...
428 views

### Data model with more outputs than inputs?

I am working on parametric studies in physics simulations, i.e. I vary some real input parameters (e.g. x0,x1,x2,x3) and get an output with a larger size (e.g. y0,y1 ... y100). Assuming that I have a ...
1 vote
34 views

### 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 ...
1 vote
176 views

### 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....
73 views

### Hyperparameter tuning of neural networks using Bayesian Optimization

One of the assumptions for finding good hyperparameters using Bayesian optimization (GP) is that the unknown function is smooth. Is this assumption valid for neural networks or at least for most of ...
42 views

### 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, ...
105 views

### 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 ...
1 vote
292 views

### 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 ...
39 views

### Advice on machine learning for small inputs and outputs

I am planning on using a machine learning algorithm to learn the mapping between sets of four coordinates (x,y,z + a distance d ...
44 views

### What is a "variable index" in the Gaussian perspective?

I was going through this article about Gaussian processes, in which the author explains about the "variable index" in the form of a plot while writing about 2D Gaussian. The explanation and plot are ...
56 views

### 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 ...
1 vote
67 views

### 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 ...