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_From @bob-carpenter on January 3, 2015 2:22_
Need utilities for Kroneckers for Gaussian processes. I don't know much about it but want to get it down as an issue and point to Ben's R example:
http…
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### Describe the workflow you want to enable
Gaussian Process Regression in sklearn comes with the affordance to return standard deviations of the predictions in the `gpr.predict` method. The Gaussia…
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Hi.
I am running a DGP model on my Jupyterlab using your tutorial providing on:
https://docs.gpytorch.ai/en/stable/examples/05_Deep_Gaussian_Processes/Deep_Gaussian_Processes.html
The vanilla c…
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Hello everyone :)
I'm currently working on a project where I need to generate probabilistic forecasts for my time series data using the darts library. Specifically, I'm comparing the performance of…
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most random processes use normal gaussian model to draw from.
This is not good because draws can surpass meaningful thresholds: boarding times less than zero.
Current solution is poor work aro…
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I am trying to implement my own kernel for Gaussian Processes regression.
In order to do so I tried to reproduce the structure of the common kernels in sklearn.gaussian_process.kernels but with no …
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Overview of approaches to try for entrainment/detrainment closures.
Closures to be implemented and tested/calibrated. (responsible team in parenthesis)
- [ ] Linear regression (Costa,...)
- [ ]…
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I think that in case of MPI walkers it would be worth to let only one walker read the hills and communicate either just the grid or all the hills but after reading
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> It is the nature of modern theoretical work to re-discover known results in different contexts. In this case, you may see that marginalizing the likelihood over a linear model with Gaussian prior is…