Questions tagged [gaussian-process]
Gaussian processes refer to stochastic processes whose realization consists of normally distributed random variables, with the additional property that any finite collection of these random variables have a multivariate normal distribution. The machinery of Gaussian processes can be employed in regression and classification problems.
1,102 questions
1 vote
0 answers
76 views
Estimation of many Gaussian process hyper-parameters
I am working with a Gaussian process $(X_t(x))_{x \in [0,1], t \geq 0}$ which evolves jointly in space and time. I know the statistics of this process: $\mathbf{E} X_t(x) = X_0(x) e^{-\mu r t} + (1-e^{...
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2 votes
0 answers
34 views
Modeling Data Using a Hybrid Parametric and Non-Parametric Approach
I'm trying model a multivariate dataset using a hybrid of a Gaussian process and a parametric model. My dataset is a function of two variables, $m$ and $p$. I expect that the $m$-dependence is well ...
- 121
1 vote
0 answers
60 views
Impact of Full Probability Distribution in GP Regression on Optimisation
In the context of an engineering design project that requires determining optimal design configurations (e.g., finding optimal design configurations of nozzle that maximise thrust ratio and discharge ...
- 11
1 vote
0 answers
69 views
Training hyperparameters of a Gaussian process with stochastic gradient descent
When training a neural net with stochastic gradient descent (SGD), I can see why it's valid to iteratively train over each data point in turn. However, doing this with a Gaussian process seems wrong, ...
- 113
4 votes
1 answer
85 views
Defining epistemic and aleatoric uncertainty in GPR
Gaussian Process Regression (GPR) enables uncertainty quantification by modeling the posterior distribution of functions. Given observed data, the latent function is the mean of the posterior ...
- 145
3 votes
1 answer
189 views
Gaussian process regression when the function output is not directly observable
I have a situation where I have a number of entities, and wish to model a function which maps the feature of each entity to an output value ($f(\mathbf{x}_i) = \hat{y}_i$). However, I do not have the ...
- 1,098
1 vote
1 answer
70 views
Mechanistic parameter inference via emulator / surrogate model
$\def\X{\vec X}\def\Y{\vec Y}$ We have a semi-expensive stochastic model $M: \X \mapsto \Y$ having ~10 continuous-valued parameters $\X$ (e.g. mean rates of state change) and producing ~10 continuous-...
- 301
1 vote
0 answers
36 views
How to model both clustering and inhibition between marks in multitype point patterns (spatial transcriptomics)?
I have a large dataset from spatial transcriptomics. This is essentially a dense (>c.75million) multitype (~500) marked point pattern dataset where each mark is a gene label, and each point is ...
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