Questions tagged [gaussian-process]

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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 ...
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5 views

Incremental Hyperparameter optimization in GPy classifier

Is there any way to do an epoch-wise incremental gradient descent hyperparameter optimization for the Gaussian Process class GPy.core.gp under the GPy package? I am familiar with the complete ...
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1answer
14 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?
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27 views

Gaussian Mixture Implementation and Optical Recognition of Handwritten Digits Data Set

Trying to implement Gaussian Mixture model implementation in python using the Optical Recognition of Handwritten Digits Data Set which consists of 10 training folds each of size $\left[100x64\right]$, ...
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1answer
42 views

VC dimension for Gaussian Process Regression [closed]

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 * (\...
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0answers
52 views

How do you choose a kernel for a discontinuous function in Gaussian Process Regression? [closed]

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 ...
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0answers
32 views

Predicting algorithm elapsed time using a Gaussian Process

I am new to the Gaussian Processes. I have a simple problem: given some training data of an algorithm performance given its problem size, I want to predict how long it will take (in seconds) to ...
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0answers
22 views

Doing my first Gaussian Process Regression using scikit-learn: Something's wrong with my Covariance function

I'm trying to do my first Gaussian Process Regression using scikit-learn. Here's my MWE: ...
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0answers
15 views

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/...
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0answers
17 views

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. ...
-1
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1answer
69 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 ...
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0answers
23 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 ...
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0answers
76 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....
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1answer
42 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 ...
3
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0answers
27 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, ...
2
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1answer
45 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 ...
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0answers
273 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 ...
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1answer
31 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 ...
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1answer
34 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 ...
4
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2answers
46 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
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2answers
51 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 ...
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0answers
140 views

Has anybody used alternative hyperparameter optimization techniques (other than default one) in SK-Learn?

I've been using Sklearn for Gaussian process regression that has L-BFGS-B (“fmin_l_bfgs_b”) as a default optimization algorithm. I want to implement some other ...
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1answer
869 views

How to combine different kernels for Gaussian process in GPyTorch?

I am trying to learn gaussian process by using GPyTorch to fit a Gaussian Process Regression model. However, I can't figure out ...