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### Description
failed conversion - missing font, I think
### (Optional:) Please add any files, screenshots, or other information here.
_No response_
### (Required) What is this issue most closely…
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Theorem:
For a stationary Gaussian process X(t) with covariance function R(t) = J₀(t), where J₀ is the Bessel function of the first kind of order 0, the expected number of zeros per unit length along …
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### Reason/inspiration (optional)
We would like a new entry on the `Gaussian Processes` concept under Sklearn. The entry should go in a new file under `docs/content/sklearn/concepts/gaussian-processe…
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Thank you for your insightful comments and for sharing your specific application. Your observation about the fundamental nature of mathematics and its application in physics is astute. Let's delve in…
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1. Kolmogorov Continuity Theorem:
This theorem provides sufficient conditions for a stochastic process to have continuous sample paths.
Theorem: Let {X(t), t ∈ T} be a stochastic process on a prob…
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Hello,
I am quite new to the package, so it is likely that I am missing something obvious (my sincerest apologies if this is the case). I have not been able to find an explanation for what I observ…
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Right now, only the exponentiated-quadratic kernel is supported as it has a native implementation in Stan. However, there seem to be quite a few other kernels worth considering. This issue is ment to …
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**Task Details**
the task is to generate the performance plots after training the diffusion model
**TODOs**
- [ ] inside main.py , use irene.methods.MyAlgorithm and get the relevant fina…
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Explore how it performs against other algorithms, both in the sparse setting (using inducing points) and non-sparse and with gaussian and non-gaussian likelihoods