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# Description
## WHAT PROBLEM ARE YOU TRYING TO SOLVE?
We consider the problem of finding high-dimensional integrals by quantum Monte Carlo methods. When solving high-dimensional problems using cl…
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1) training on fragmentet training sample, about the monte carlo integration
2) shear on background, not connected one to one with the light
3) polish A3
4) downsizing the datasets.
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# Bug Report
## Description
The codeblock coloring renders emojicode operations (✖️, ➕, ✖️,◀️...) unreadable.
### Steps to Reproduce
Steps to reproduce the behavior:
1. Go to https://ww…
leios updated
5 years ago
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Can't this be solved straightforwardly with integration (including Monte Carlo integration, if appropriate)?
Define the probability of occurrence (p) as π/Z, where π is our model output. π is propor…
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## Describe the bug
Running `evaluate_uncertainties.py` with any of its 3 methods (monte carlo, sobol, morris method) does not produce expected output.
When running `evaluate_uncertainties.py`…
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Since alpaka has support for both random number generators and atomic operations, it should be easy to make an example implementing Monte Carlo. To me two applications seem most fitting:
* [numerical…
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The current method is sensitive to noise, such that if there is any error in GP termination at the nuclear CP, bits of the nuclear region can be double-counted (or more), leading to huge overestimates…
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Right now the bottleneck is the creation of the data for the IPW numerator / Monte Carlo integration. This should be easy to speed up (via `Pool`) or using some clever tricks in generation. The real i…
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Possibly implement a Mersenne twister test suite (Verilog version: https://github.com/alexforencich/verilog-mersenne)
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I couldn't find anything about the technique used by GPy when optimising the length-scales in an RBF kernel with ARD=True.
I'm aware of Log-likelihood optimisation (methods such as conjugate gradie…